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Neural Volumetric Object Selection

Authors :
Ren, Zhongzheng
Agarwala, Aseem
Russell, Bryan
Schwing, Alexander G.
Wang, Oliver
Publication Year :
2022

Abstract

We introduce an approach for selecting objects in neural volumetric 3D representations, such as multi-plane images (MPI) and neural radiance fields (NeRF). Our approach takes a set of foreground and background 2D user scribbles in one view and automatically estimates a 3D segmentation of the desired object, which can be rendered into novel views. To achieve this result, we propose a novel voxel feature embedding that incorporates the neural volumetric 3D representation and multi-view image features from all input views. To evaluate our approach, we introduce a new dataset of human-provided segmentation masks for depicted objects in real-world multi-view scene captures. We show that our approach out-performs strong baselines, including 2D segmentation and 3D segmentation approaches adapted to our task.<br />Comment: CVPR 2022 camera ready

Details

Database :
arXiv
Publication Type :
Report
Accession number :
edsarx.2205.14929
Document Type :
Working Paper