Neural Volumetric Object Selection

• URL: http://arxiv.org/abs/2205.14929v1
• Date: Mon, 30 May 2022 08:55:20 GMT
• Title: Neural Volumetric Object Selection
• Authors: Zhongzheng Ren and Aseem Agarwala and Bryan Russell and Alexander G. Schwing and Oliver Wang
• Abstract summary: 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.
• Score: 126.04480613166194
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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