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Point cloud matching is an important procedure in a variety of computer vision tasks. Traditional point cloud matching methods have made great progress, while neural network‐based approaches are becoming a trend, powered by their strong capabilities of feature extraction. Existing point matching neural networks, however, mainly focus on the rigid transformation. More complex transformations should also be considered in many scenarios. In this regard, the authors extend the rigid registration to non‐rigid cases and propose a network called the Scale Robust Point Matching (SRPM)‐Net for scale point matching. This robust structure‐preserving network is implemented by incorporating Lie group parametrisation. It is conducted by Lie group linearisation representation with the constraints of parameters under the corresponding basis of Lie algebra. SRPM‐Net preserves the structure of the solution and avoids degeneration. The contributions of this paper lie in two aspects: Most importantly, SRPM‐Net provides an extendable framework for handling complicated transformations. Secondly, it introduces a new feature learning module, which better preserves the shape structure by aggregating the high‐dimensional feature and calculating the normal vector of point cloud surface automatically. Experimental results show that SRPM‐Net is more robust and accurate than existing traditional and recent deep learning methods under various situations. [ABSTRACT FROM AUTHOR]

• CAB-UNet: an end-to-end Category Attention Boosting algorithm. • A gradient boosting and deep learning method for 3D medical image segmentation. • Verified on HVSMR 2016 and MM-WHS 2017 Challenge datasets. • Outperform the state-of-the-art algorithms. With the development of machine learning and artificial intelligence, many convolutional neural networks (CNNs) based segmentation methods have been proposed for 3D cardiac segmentation. In this paper, we propose the category attention boosting (CAB) module, which combines the deep network calculation graph with the boosting method. On the one hand, we add the attention mechanism into the gradient boosting process, which enhances the information of coarse segmentation without high computation cost. On the other hand, we introduce the CAB module into the 3D U-Net segmentation network and construct a new multi-scale boosting model CAB U-Net which strengthens the gradient flow in the network and makes full use of the low resolution feature information. Thanks to the advantage that end-to-end networks can adaptively adjust the internal parameters, CAB U-Net can make full use of the complementary effects among different base learners. Extensive experiments on public datasets show that our approach can achieve superior performance over the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

In the field of autonomous driving, carriers are equipped with a variety of sensors, including cameras and LiDARs. However, the camera suffers from problems of illumination and occlusion, and the LiDAR encounters motion distortion, degenerate environment and limited ranging distance. Therefore, fusing the information from these two sensors deserves to be explored. In this paper, we propose a fusion network which robustly captures both the image and point cloud descriptors to solve the place recognition problem. Our contribution can be summarized as: (1) applying the trimmed strategy in the point cloud global feature aggregation to improve the recognition performance, (2) building a compact fusion framework which captures both the robust representation of the image and 3D point cloud, and (3) learning a proper metric to describe the similarity of our fused global feature. The experiments on KITTI and KAIST datasets show that the proposed fused descriptor is more robust and discriminative than the single sensor descriptor. [ABSTRACT FROM AUTHOR]