Search

In this paper, we introduce DCDepth, a novel framework for the long-standing monocular depth estimation task. Moving beyond conventional pixel-wise depth estimation in the spatial domain, our approach estimates the frequency coefficients of depth patches after transforming them into the discrete cosine domain. This unique formulation allows for the modeling of local depth correlations within each patch. Crucially, the frequency transformation segregates the depth information into various frequency components, with low-frequency components encapsulating the core scene structure and high-frequency components detailing the finer aspects. This decomposition forms the basis of our progressive strategy, which begins with the prediction of low-frequency components to establish a global scene context, followed by successive refinement of local details through the prediction of higher-frequency components. We conduct comprehensive experiments on NYU-Depth-V2, TOFDC, and KITTI datasets, and demonstrate the state-of-the-art performance of DCDepth. Code is available at https://github.com/w2kun/DCDepth., Comment: Accepted by NeurIPS-2024

Context. The duration of star formation (SF) in globular clusters (GCs) is an essential aspect for understanding their formation. Contrary to previous presumptions that all stars above 8 M explode as core-collapse supernovae (CCSNe), recent evidence suggests a more complex scenario. Aims. We analyse iron spread observations from 55 GCs to estimate the number of CCSNe explosions before SF termination, thereby determining the SF duration. This work for the first time takes the possibility of failed CCSNe into account, when estimating the SF duration. Methods. Two scenarios are considered: one where all stars explode as CCSNe and another where only stars below 20 M lead to CCSNe, as most CCSN models predict that no failed CCSNe happen below 20 M . Results. This establishes a lower ($\approx$ 3.5 Myr) and an upper ($\approx$ 10.5 Myr) limit for the duration of SF. Extending the findings of our previous paper, this study indicates a significant difference in SF duration based on CCSN outcomes, with failed CCSNe extending SF by up to a factor of three. Additionally, a new code is introduced to compute the SF duration for a given CCSN model. Conclusions. The extended SF has important implications on GC formation, including enhanced pollution from stellar winds and increased binary star encounters. These results underscore the need for a refined understanding of CCSNe in estimating SF durations and the formation of multiple stellar populations in GCs., Comment: 9 pages, 9 figures, accepted by A&A

Recent RGB-guided depth super-resolution methods have achieved impressive performance under the assumption of fixed and known degradation (e.g., bicubic downsampling). However, in real-world scenarios, captured depth data often suffer from unconventional and unknown degradation due to sensor limitations and complex imaging environments (e.g., low reflective surfaces, varying illumination). Consequently, the performance of these methods significantly declines when real-world degradation deviate from their assumptions. In this paper, we propose the Degradation Oriented and Regularized Network (DORNet), a novel framework designed to adaptively address unknown degradation in real-world scenes through implicit degradation representations. Our approach begins with the development of a self-supervised degradation learning strategy, which models the degradation representations of low-resolution depth data using routing selection-based degradation regularization. To facilitate effective RGB-D fusion, we further introduce a degradation-oriented feature transformation module that selectively propagates RGB content into the depth data based on the learned degradation priors. Extensive experimental results on both real and synthetic datasets demonstrate the superiority of our DORNet in handling unknown degradation, outperforming existing methods. The code is available at https://github.com/yanzq95/DORNet., Comment: 10 pages

The initial mass function (IMF) is one of the most important functions in astrophysics because it is key to reconstructing the cosmological matter cycle, understanding the formation of super-massive black holes, and deciphering the light from high-redshift observations. The IMF's dependency on the physical conditions of the gas and its connection to the galaxy-wide IMF connects the molecular clumps to the cosmological scale. The extraction of the IMF from observational data requires a thorough understanding of stellar evolution, the time-dependent stellar multiplicity, the stellar-dynamical evolution of dense stellar populations, and the structures, star formation histories, and chemical enrichment histories of galaxies. The IMF in galaxies, referred to as the galaxy-wide IMF (gwIMF), and the IMF in individual star-forming regions (the stellar IMF) need not be the same, although the former must be related to the latter. Observational surveys inform on whether star-forming regions provide evidence for the stellar IMF being a probability density distribution function. They may also indicate star formation to optimally follow an IMF shaped by the physical conditions of the star-forming gas. Both theoretical and observational evidence suggest a relationship between the initial mass function of brown dwarfs and that of stars. Late-type stars may arise from feedback-regulated fragmentation of molecular cloud filaments, which build up embedded clusters. In contrast, early-type stars form under more violent accretion and feedback-regulated conditions near the centers of these clusters. The integration over all star-forming molecular cloud clumps and their stellar IMFs in a galaxy via the IGIMF theory yields its gwIMF which sensitively depends on the physical properties of the molecular cloud clumps and the range of their masses that depends on the SFR of the galaxy., Comment: 36 pages, 14 figures, LaTeX. This is a pre-print of a chapter for the Encyclopedia of Astrophysics (edited by I. Mandel, section editor F.R.N. Schneider) to be published by Elsevier as a Reference Module

The task of vision-based 3D occupancy prediction aims to reconstruct 3D geometry and estimate its semantic classes from 2D color images, where the 2D-to-3D view transformation is an indispensable step. Most previous methods conduct forward projection, such as BEVPooling and VoxelPooling, both of which map the 2D image features into 3D grids. However, the current grid representing features within a certain height range usually introduces many confusing features that belong to other height ranges. To address this challenge, we present Deep Height Decoupling (DHD), a novel framework that incorporates explicit height prior to filter out the confusing features. Specifically, DHD first predicts height maps via explicit supervision. Based on the height distribution statistics, DHD designs Mask Guided Height Sampling (MGHS) to adaptively decouple the height map into multiple binary masks. MGHS projects the 2D image features into multiple subspaces, where each grid contains features within reasonable height ranges. Finally, a Synergistic Feature Aggregation (SFA) module is deployed to enhance the feature representation through channel and spatial affinities, enabling further occupancy refinement. On the popular Occ3D-nuScenes benchmark, our method achieves state-of-the-art performance even with minimal input frames. Code is available at https://github.com/yanzq95/DHD.

Recent advances in low light image enhancement have been dominated by Retinex-based learning framework, leveraging convolutional neural networks (CNNs) and Transformers. However, the vanilla Retinex theory primarily addresses global illumination degradation and neglects local issues such as noise and blur in dark conditions. Moreover, CNNs and Transformers struggle to capture global degradation due to their limited receptive fields. While state space models (SSMs) have shown promise in the long-sequence modeling, they face challenges in combining local invariants and global context in visual data. In this paper, we introduce MambaLLIE, an implicit Retinex-aware low light enhancer featuring a global-then-local state space design. We first propose a Local-Enhanced State Space Module (LESSM) that incorporates an augmented local bias within a 2D selective scan mechanism, enhancing the original SSMs by preserving local 2D dependency. Additionally, an Implicit Retinex-aware Selective Kernel module (IRSK) dynamically selects features using spatially-varying operations, adapting to varying inputs through an adaptive kernel selection process. Our Global-then-Local State Space Block (GLSSB) integrates LESSM and IRSK with LayerNorm as its core. This design enables MambaLLIE to achieve comprehensive global long-range modeling and flexible local feature aggregation. Extensive experiments demonstrate that MambaLLIE significantly outperforms state-of-the-art CNN and Transformer-based methods. Project Page: https://mamballie.github.io/anon/

Recent observations and theories have presented a strong challenge to the universality of the stellar initial mass function (IMF) in extreme environments. A notable example has been found for starburst conditions, where evidence favours a top-heavy IMF, i.e. there is a bias toward massive stars compared to the IMF that is responsible for the stellar mass function and elemental abundances observed in the Milky Way. Local starburst galaxies have star-formation rates similar to those in high-redshift main-sequence galaxies, which appear to dominate the stellar mass budget at early epochs. However, the IMF of high-redshift main-sequence galaxies is yet to be probed. Since $^{13}$CO and C$^{18}$O isotopologues are sensitive to the IMF, we have observed these lines towards four strongly-lensed high-redshift main-sequence galaxies using the Atacama Large Millimeter/sub-millimeter Array. Of our four targets, SDSS J0901+1814, at $z \approx 2.26$, is seen clearly in $^{13}$CO and C$^{18}$O, the first detection of CO isotopologues in the high-redshift main-sequence galaxy population. The observed $^{13}$C/$^{18}$O ratio, $2.4 \pm 0.8$, is significantly lower than that of local main-sequence galaxies. We estimate the isotope ratio, oxygen abundance and stellar mass using a series of chemical evolution models with varying star-formation histories and IMFs. All models favour an IMF that is more top-heavy than that of the Milky Way. Thus, as with starburst galaxies, main-sequence galaxies in the high-redshift Universe have a greater fraction of massive stars than a Milky-Way IMF would imply., Comment: 15 pages, 8 figures, accepted by ApJ

(Abridged) Observational estimates of galaxy properties rely on the inherent galaxy-wide initial mass function (gwIMF), which systematically varies with the global SFR and metallicity, as proposed by the integrated-galactic IMF (IGIMF) theory and supported by empirical evidence. We incorporate PARSEC and COLIBRI stellar isochrones into the GalIMF code, a galaxy chemical evolution (GCE) model featuring real-time updates of environment-dependent gwIMFs. This newly developed photometric GalIMF (photGalIMF) code allows the calculation of photometric properties for galaxies with diverse stellar populations. Subsequently, we analyze observed luminosities and metallicities of local star-forming galaxies to deduce their stellar masses assuming that they have constant SFRs over 13.6 Gyr. We also compute SFR$-$H$\alpha$ luminosity relations for varying stellar metallicities using a separate stellar population synthesis code based on PEGASE. Comparing the IGIMF theory to the canonical universal IMF, our analysis reveals that estimates of the stellar masses and SFRs for local star-forming galaxies differ by factors of $\approx 2$ and 10, respectively. The computed gas-depletion timescale increases with gas mass, implying lower star formation efficiencies in more massive galaxies, possibly due to stronger feedback regulation, aligning with theoretical expectations. Additionally, the characteristic stellar mass buildup timescale increases with stellar mass, indicating that massive disk galaxies initiate star formation earlier than their low-mass counterparts. The photGalIMF code enables self-consistent computations of galactic photometry, self-consistently with GCE modelling within the context of an environment-dependent gwIMF. Utilizing Ks-band and H$\alpha$ luminosities of galaxies, the outcomes include galaxy mass, SFR, and fitting functions for the SFR correction factor., Comment: Accepted for publication in Astronomy & Astrophysics (A&A), 19 pages, 11 figures, 2 tables. The photGalIMF code is publicly available on GitHub: https://github.com/juzikong/photGalIMF

The Stellar Initial Mass Function (IMF) characterizes the mass distribution of newly formed stars in various cosmic environments, serving as a fundamental assumption in astrophysical research. Recent findings challenge the prevalent notion of a universal and static IMF, proposing instead that the IMF's shape is contingent upon the star formation environment. In this study, we analyze the galaxy-wide variation of the IMF for low-mass stars in both dwarf and massive galaxies with diverse observational methods. Despite systematic discrepancies between different approaches, an IMF model with a metallicity-dependent slope for the low-mass stars aligns with the majority of observations, indicating a high degree of uniformity in the star formation processes across the universe. We also emphasize the need for a more comprehensive understanding of the variation of the low-mass IMF, considering measurement biases and factors beyond metallicity., Comment: 15 pages, 5 figures, accepted for publication in The Astrophysical Journal

Depth completion is a vital task for autonomous driving, as it involves reconstructing the precise 3D geometry of a scene from sparse and noisy depth measurements. However, most existing methods either rely only on 2D depth representations or directly incorporate raw 3D point clouds for compensation, which are still insufficient to capture the fine-grained 3D geometry of the scene. To address this challenge, we introduce Tri-Perspective view Decomposition (TPVD), a novel framework that can explicitly model 3D geometry. In particular, (1) TPVD ingeniously decomposes the original point cloud into three 2D views, one of which corresponds to the sparse depth input. (2) We design TPV Fusion to update the 2D TPV features through recurrent 2D-3D-2D aggregation, where a Distance-Aware Spherical Convolution (DASC) is applied. (3) By adaptively choosing TPV affinitive neighbors, the newly proposed Geometric Spatial Propagation Network (GSPN) further improves the geometric consistency. As a result, our TPVD outperforms existing methods on KITTI, NYUv2, and SUN RGBD. Furthermore, we build a novel depth completion dataset named TOFDC, which is acquired by the time-of-flight (TOF) sensor and the color camera on smartphones. Project page: https://yanzq95.github.io/projectpage/TOFDC/index.html, Comment: Accepted to CVPR 2024

This work analyses the present-day mass function (PDMF) of 93~star clusters utilizing Gaia DR3 data, with membership determined by the StarGo machine learning algorithm. The impact of unresolved binary systems on mass estimation is rigorously assessed, adopting three mass ratio profiles for correction. The PDMF is characterized by the power-law index, $\alpha$, derived through a robust maximum likelihood method that avoids biases associated with data binning. The value of $\alpha$ for stars between the completeness limited mass of Gaia with a mean 0.3 $M_\odot$ for our cluster samples and 2 $M_\odot$, exhibits stability for clusters younger than 200 Myr, decreasing for older clusters, particularly when considering stars within the half-mass radius. The PDMF of these star clusters is consistent with a dynamically evolved Kroupa IMF via the loss of low-mass stars. Cluster morphology shows a correlation with $\alpha$, as $\alpha$ values exhibit a decreasing trend from filamentary to tidal-tail clusters, mirroring the sequence of increasing cluster age. The dependence of $\alpha$ on total cluster mass is weak, with a subtle increase for higher-mass clusters, especially outside the half-mass radius. We do not observe a correlation between $\alpha$ and the mean metallicity of the clusters. Younger clusters have lower metallicity compared to their older counterparts, which indicates that the older clusters might have migrated to the solar neighbourhood from the inner disk. A comparison with numerical models incorporating a black hole population suggests the need for observations of distant, older, massive open clusters to determine whether or not they contain black holes., Comment: 19 pages, 10 figures, accepted to ApJ

Multi-modal fusion is vital to the success of super-resolution of depth maps. However, commonly used fusion strategies, such as addition and concatenation, fall short of effectively bridging the modal gap. As a result, guided image filtering methods have been introduced to mitigate this issue. Nevertheless, it is observed that their filter kernels usually encounter significant texture interference and edge inaccuracy. To tackle these two challenges, we introduce a Scene Prior Filtering network, SPFNet, which utilizes the priors surface normal and semantic map from large-scale models. Specifically, we design an All-in-one Prior Propagation that computes the similarity between multi-modal scene priors, i.e., RGB, normal, semantic, and depth, to reduce the texture interference. In addition, we present a One-to-one Prior Embedding that continuously embeds each single-modal prior into depth using Mutual Guided Filtering, further alleviating the texture interference while enhancing edges. Our SPFNet has been extensively evaluated on both real and synthetic datasets, achieving state-of-the-art performance., Comment: 13 pages

The star formation histories (SFHs) of galaxies provide valuable insights into galaxy evolution and stellar physics. Understanding the SFHs enables the study of chemical enrichment of galaxies, star formation triggered by interactions, and the behavior of various stellar populations. This work investigates the SFHs of ten dwarf galaxies in the Local Group (LG), which spans a wide range of types, masses, luminosities, and metallicities. The analysis is based on our new sample of the member stars in the LG after removing the foreground dwarf stars by the near-infrared color-color diagram and the Gaia astrometric information. The samples include the most complete and pure red supergiants and asymptotic giant branch stars to gain valuable insights into the recent SFHs of the galaxies. The CMD fitting method is introduced to measure the SFH. The Padova isochrones are used to generate initial model CMDs, accounting for photometric errors and completeness through star field simulations to match the completeness and error distributions of the observed CMDs. Subsequently, the SFHs, distance modulus, and metallicity of the ten dwarf galaxies are determined by fitting the CMDs. The results indicate that the star formation rates (SFRs) of dwarf irregulars show a gradual increase, while those of dwarf ellipticals exhibit a gradual decrease from the past to the present. Furthermore, this work shows that the star formation activity in dwarf ellipticals persisted up to 30 Myr ago. A significant increasing feature in the SFH of NGC 6822 reveals star formation activity triggered by an interaction event., Comment: 21 pages, 11 figures, 5 tables, accepted for publication in ApJ

Depth super-resolution (DSR) aims to restore high-resolution (HR) depth from low-resolution (LR) one, where RGB image is often used to promote this task. Recent image guided DSR approaches mainly focus on spatial domain to rebuild depth structure. However, since the structure of LR depth is usually blurry, only considering spatial domain is not very sufficient to acquire satisfactory results. In this paper, we propose structure guided network (SGNet), a method that pays more attention to gradient and frequency domains, both of which have the inherent ability to capture high-frequency structure. Specifically, we first introduce the gradient calibration module (GCM), which employs the accurate gradient prior of RGB to sharpen the LR depth structure. Then we present the Frequency Awareness Module (FAM) that recursively conducts multiple spectrum differencing blocks (SDB), each of which propagates the precise high-frequency components of RGB into the LR depth. Extensive experimental results on both real and synthetic datasets demonstrate the superiority of our SGNet, reaching the state-of-the-art. Codes and pre-trained models are available at https://github.com/yanzq95/SGNet., Comment: Accepted to AAAI 2024

Depth completion aims to recover dense depth maps from sparse ones, where color images are often used to facilitate this task. Recent depth methods primarily focus on image guided learning frameworks. However, blurry guidance in the image and unclear structure in the depth still impede their performance. To tackle these challenges, we explore a repetitive design in our image guided network to gradually and sufficiently recover depth values. Specifically, the repetition is embodied in both the image guidance branch and depth generation branch. In the former branch, we design a dense repetitive hourglass network (DRHN) to extract discriminative image features of complex environments, which can provide powerful contextual instruction for depth prediction. In the latter branch, we present a repetitive guidance (RG) module based on dynamic convolution, in which an efficient convolution factorization is proposed to reduce the complexity while modeling high-frequency structures progressively. Furthermore, in the semantic guidance branch, we utilize the well-known large vision model, i.e., segment anything (SAM), to supply RG with semantic prior. In addition, we propose a region-aware spatial propagation network (RASPN) for further depth refinement based on the semantic prior constraint. Finally, we collect a new dataset termed TOFDC for the depth completion task, which is acquired by the time-of-flight (TOF) sensor and the color camera on smartphones. Extensive experiments demonstrate that our method achieves state-of-the-art performance on KITTI, NYUv2, Matterport3D, 3D60, VKITTI, and our TOFDC., Comment: 20 pages

Neural Radiance Fields (NeRF) have shown promise in generating realistic novel views from sparse scene images. However, existing NeRF approaches often encounter challenges due to the lack of explicit 3D supervision and imprecise camera poses, resulting in suboptimal outcomes. To tackle these issues, we propose AltNeRF -- a novel framework designed to create resilient NeRF representations using self-supervised monocular depth estimation (SMDE) from monocular videos, without relying on known camera poses. SMDE in AltNeRF masterfully learns depth and pose priors to regulate NeRF training. The depth prior enriches NeRF's capacity for precise scene geometry depiction, while the pose prior provides a robust starting point for subsequent pose refinement. Moreover, we introduce an alternating algorithm that harmoniously melds NeRF outputs into SMDE through a consistence-driven mechanism, thus enhancing the integrity of depth priors. This alternation empowers AltNeRF to progressively refine NeRF representations, yielding the synthesis of realistic novel views. Extensive experiments showcase the compelling capabilities of AltNeRF in generating high-fidelity and robust novel views that closely resemble reality., Comment: Accepted by AAAI-24

Depth completion is the task of recovering dense depth maps from sparse ones, usually with the help of color images. Existing image-guided methods perform well on daytime depth perception self-driving benchmarks, but struggle in nighttime scenarios with poor visibility and complex illumination. To address these challenges, we propose a simple yet effective framework called LDCNet. Our key idea is to use Recurrent Inter-Convolution Differencing (RICD) and Illumination-Affinitive Intra-Convolution Differencing (IAICD) to enhance the nighttime color images and reduce the negative effects of the varying illumination, respectively. RICD explicitly estimates global illumination by differencing two convolutions with different kernels, treating the small-kernel-convolution feature as the center of the large-kernel-convolution feature in a new perspective. IAICD softly alleviates local relative light intensity by differencing a single convolution, where the center is dynamically aggregated based on neighboring pixels and the estimated illumination map in RICD. On both nighttime depth completion and depth estimation tasks, extensive experiments demonstrate the effectiveness of our LDCNet, reaching the state of the art., Comment: 8 pages

Reconstructing category-specific objects from a single image is a challenging task that requires inferring the geometry and appearance of an object from a limited viewpoint. Existing methods typically rely on local feature retrieval based on re-projection with known camera intrinsic, which are slow and prone to distortion at viewpoints distant from the input image. In this paper, we present Variable Radiance Field (VRF), a novel framework that can efficiently reconstruct category-specific objects from a single image without known camera parameters. Our key contributions are: (1) We parameterize the geometry and appearance of the object using a multi-scale global feature extractor, which avoids frequent point-wise feature retrieval and camera dependency. We also propose a contrastive learning-based pretraining strategy to improve the feature extractor. (2) We reduce the geometric complexity of the object by learning a category template, and use hypernetworks to generate a small neural radiance field for fast and instance-specific rendering. (3) We align each training instance to the template space using a learned similarity transformation, which enables semantic-consistent learning across different objects. We evaluate our method on the CO3D dataset and show that it outperforms existing methods in terms of quality and speed. We also demonstrate its applicability to shape interpolation and object placement tasks.

The stellar initial mass function (IMF) is commonly interpreted to be a scale-invariant probability density distribution function (PDF) such that many small clusters yield the same IMF as one massive cluster of the same combined number of stars. Observations of the galaxy-wide IMF challenge this as dwarf galaxies do not form as many massive stars as expected. This indicates a highly self-regulated star formation process in which stellar masses are not stochastically sampled from the IMF and are instead related to the environment of star formation. Here, the nature of star formation is studied using the relation between the most massive star born in a star cluster and its parental stellar cluster mass (the $m_{\rm max}$--$M_{\rm ecl}$ relation). This relation has been argued to be a statistical effect if stars are sampled randomly from the IMF. By comparing the tightness of the observed $m_{\rm max}$--$M_{\rm ecl}$ distribution with synthetic star clusters with stochastically sampled stellar masses, we find that the expected dispersion of the mock observations is much larger than the observed dispersion. Assuming that $m_{\rm max}$ and $M_{\rm ecl}$ uncertainties from the literature are correct, our test rejects the hypothesis that the IMF is a PDF at a more than $4.5\sigma$ confidence level. Alternatively, we provide a deterministic stellar mass sampling tool which reproduces the observed $m_{\rm max}$--$M_{\rm ecl}$ distribution and compares well with the luminosities of star-forming molecular clumps. In addition, we find that there is a significant flattening of the $m_{\rm max}$--$M_{\rm ecl}$ relation near $m_{\rm max}=13~M_\odot$. This may suggest strong feedback of stars more massive than about $13~M_\odot$ and/or the ejections of the most massive stars from young clusters in the mass range 63 to $400~M_\odot$ to be likely important physical processes in forming clusters., Comment: 16 pages, 10 figures. Accepted for publication in A&A

Unsupervised depth completion aims to recover dense depth from the sparse one without using the ground-truth annotation. Although depth measurement obtained from LiDAR is usually sparse, it contains valid and real distance information, i.e., scale-consistent absolute depth values. Meanwhile, scale-agnostic counterparts seek to estimate relative depth and have achieved impressive performance. To leverage both the inherent characteristics, we thus suggest to model scale-consistent depth upon unsupervised scale-agnostic frameworks. Specifically, we propose the decomposed scale-consistent learning (DSCL) strategy, which disintegrates the absolute depth into relative depth prediction and global scale estimation, contributing to individual learning benefits. But unfortunately, most existing unsupervised scale-agnostic frameworks heavily suffer from depth holes due to the extremely sparse depth input and weak supervised signal. To tackle this issue, we introduce the global depth guidance (GDG) module, which attentively propagates dense depth reference into the sparse target via novel dense-to-sparse attention. Extensive experiments show the superiority of our method on outdoor KITTI benchmark, ranking 1st and outperforming the best KBNet more than 12% in RMSE. In addition, our approach achieves state-of-the-art performance on indoor NYUv2 dataset., Comment: Accepted by AAAI2023

Owing to the recent identification of major substructures in our Milky Way (MW), the astronomical community has started to reevaluate the importance of dissolved and existing dwarf galaxies. In this work, we investigate up to 13 elements in 43 giant stars of the Sculptor dwarf galaxy (Scl) using high-signal-to-noise-ratio near-infrared (NIR) APOGEE spectra. Thanks to the strong feature lines in the NIR, we were able to determine high-resolution O, Si, and Al abundances for a large group of sample stars for the first time in Scl. By comparing the [$\alpha$/Fe] (i.e., O, Mg, Si, Ca, and Ti) of the stars in Scl, Sagittarius, and the MW, we confirm the general trend that less massive galaxies tend to show lower [$\alpha$/Fe]. The low [Al/Fe] ($\sim -0.5$) in Scl demonstrates the value of this ratio as a discriminator with which to identify stars born in dwarf galaxies (from MW field stars). A chemical-evolution model suggests that Scl has a top-light initial mass function (IMF), with a high-mass IMF power index of $\sim -2.7$, and a minimum Type Ia supernovae delay time of $\sim 100$ Myr. Furthermore, a linear regression analysis indicates a negative radial metallicity gradient and positive radial gradients for [Mg/Fe] and [Ca/Fe], in qualitative agreement with the outside-in formation scenario., Comment: A&A accepted

Recent results suggest that the initial mass function (IMF) of globular clusters (GCs) is metallicity and density dependent. Here it is studied how this variation affects the initial masses and the numbers of core collapse supernovae (CCSNe) required to reproduce the observed iron spreads in GCs. The IMFs of all of the investigated GCs were top-heavy implying larger initial masses compared to previous results computed assuming an invariant canonical IMF. This leads to more CCSNe being required to explain the observed iron abundance spreads. The results imply that the more massive GCs formed at smaller Galactocentric radii, possibly suggesting in-situ formation of the population II halo. The time until star formation (SF) ended within a proto-GC is computed to be 3.5 - 4 Myr, being slightly shorter than the 4 Myr obtained using the canonical IMF. Therefore, the impact of the IMF on the time for which SF lasts is small., Comment: published in MNRAS, 13 pages, 12 figures

Perceiving the three-dimensional (3D) structure of the spacecraft is a prerequisite for successfully executing many on-orbit space missions, and it can provide critical input for many downstream vision algorithms. In this paper, we propose to sense the 3D structure of spacecraft using light detection and ranging sensor (LIDAR) and a monocular camera. To this end, Spacecraft Depth Completion Network (SDCNet) is proposed to recover the dense depth map based on gray image and sparse depth map. Specifically, SDCNet decomposes the object-level spacecraft depth completion task into foreground segmentation subtask and foreground depth completion subtask, which segments the spacecraft region first and then performs depth completion on the segmented foreground area. In this way, the background interference to foreground spacecraft depth completion is effectively avoided. Moreover, an attention-based feature fusion module is also proposed to aggregate the complementary information between different inputs, which deduces the correlation between different features along the channel and the spatial dimension sequentially. Besides, four metrics are also proposed to evaluate object-level depth completion performance, which can more intuitively reflect the quality of spacecraft depth completion results. Finally, a large-scale satellite depth completion dataset is constructed for training and testing spacecraft depth completion algorithms. Empirical experiments on the dataset demonstrate the effectiveness of the proposed SDCNet, which achieves 0.25m mean absolute error of interest and 0.759m mean absolute truncation error, surpassing state-of-the-art methods by a large margin. The spacecraft pose estimation experiment is also conducted based on the depth completion results, and the experimental results indicate that the predicted dense depth map could meet the needs of downstream vision tasks., Comment: the preprint version submitted to IEEE Transactions on Aerospace and Electronic Systems

In this paper, we formulate a potentially valuable panoramic depth completion (PDC) task as panoramic 3D cameras often produce 360{\deg} depth with missing data in complex scenes. Its goal is to recover dense panoramic depths from raw sparse ones and panoramic RGB images. To deal with the PDC task, we train a deep network that takes both depth and image as inputs for the dense panoramic depth recovery. However, it needs to face a challenging optimization problem of the network parameters due to its non-convex objective function. To address this problem, we propose a simple yet effective approach termed M{^3}PT: multi-modal masked pre-training. Specifically, during pre-training, we simultaneously cover up patches of the panoramic RGB image and sparse depth by shared random mask, then reconstruct the sparse depth in the masked regions. To our best knowledge, it is the first time that we show the effectiveness of masked pre-training in a multi-modal vision task, instead of the single-modal task resolved by masked autoencoders (MAE). Different from MAE where fine-tuning completely discards the decoder part of pre-training, there is no architectural difference between the pre-training and fine-tuning stages in our M$^{3}$PT as they only differ in the prediction density, which potentially makes the transfer learning more convenient and effective. Extensive experiments verify the effectiveness of M{^3}PT on three panoramic datasets. Notably, we improve the state-of-the-art baselines by averagely 26.2% in RMSE, 51.7% in MRE, 49.7% in MAE, and 37.5% in RMSElog on three benchmark datasets., Comment: Accepted by ECCV2022

The presented study gives a comprehensive overview of the theory and the evidence for a systematically varying stellar initial mass function (IMF). Then we focus on the impact of this paradigm change, that is, from the universal invariant IMF to a variable IMF, on galaxy chemical evolution (GCE) studies. For this aim, we developed the first GCE code, GalIMF, that is able to incorporate the empirically calibrated environment-dependent IMF variation theory, the integrated galactic initial mass function (IGIMF) theory. In this theory, the galaxy-wide IMF is calculated by summing all the IMFs in all embedded star clusters which formed throughout the galaxy in 10 Myr time epochs. The GalIMF code recalculates the galaxy-wide IMF at each time step because the integrated galaxy-wide IMF depends on the galactic star formation rate and metallicity. The resulting galaxy-wide IMF and metal abundance evolve with time. Using this code, we examine the chemical evolution of early-type galaxies (ETGs) from dwarf to the most massive. We find that the introduction of the non-canonical IMF affects the best estimation of the galaxy properties such as their mass, star formation history, and star formation efficiency. Moreover, we are able to provide an independent estimation on the stellar formation timescale of galaxies, the type Ia supernova production efficiency, and constrain the IMF variation law of the low-mass stars. This work provides to the community the publicly available GalIMF code with improved constraints on the IMF variation and has, for the first time, resolved the discrepancy between the galaxy formation timescales obtained from stellar population synthesis and chemical enrichment studies., Comment: Abstract of the doctoral thesis, 41 pages, 5 figures