NeRF-RPN: A general framework for object detection in NeRFs

• URL: http://arxiv.org/abs/2211.11646v3
• Date: Mon, 27 Mar 2023 16:40:30 GMT
• Title: NeRF-RPN: A general framework for object detection in NeRFs
• Authors: Benran Hu, Junkai Huang, Yichen Liu, Yu-Wing Tai, Chi-Keung Tang
• Abstract summary: NeRF-RPN aims to detect all bounding boxes of objects in a scene. NeRF-RPN is a general framework and can be applied to detect objects without class labels.
• Score: 54.54613914831599
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper presents the first significant object detection framework, NeRF-RPN, which directly operates on NeRF. Given a pre-trained NeRF model, NeRF-RPN aims to detect all bounding boxes of objects in a scene. By exploiting a novel voxel representation that incorporates multi-scale 3D neural volumetric features, we demonstrate it is possible to regress the 3D bounding boxes of objects in NeRF directly without rendering the NeRF at any viewpoint. NeRF-RPN is a general framework and can be applied to detect objects without class labels. We experimented NeRF-RPN with various backbone architectures, RPN head designs and loss functions. All of them can be trained in an end-to-end manner to estimate high quality 3D bounding boxes. To facilitate future research in object detection for NeRF, we built a new benchmark dataset which consists of both synthetic and real-world data with careful labeling and clean up. Code and dataset are available at https://github.com/lyclyc52/NeRF_RPN.

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