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arXiv · 2211.11646

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

Benran Hu·Junkai Huang·Yichen Liu·Yu-Wing Tai·Chi-Keung Tang
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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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Benran Hu, Junkai Huang, Yichen Liu, Yu-Wing Tai, Chi-Keung Tang. 2022-11-21. NeRF-RPN: A general framework for object detection in NeRFs. https://arxiv.org/abs/2211.11646

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