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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 exploitinga novel voxel representation that incorporates multi-scale 3D neural volumetricfeatures, we demonstrate it is possible to regress the 3D bounding boxes ofobjects in NeRF directly without rendering the NeRF at any viewpoint. NeRF-RPNis a general framework and can be applied to detect objects without classlabels. We experimented NeRF-RPN with various backbone architectures, RPN headdesigns and loss functions. All of them can be trained in an end-to-end mannerto estimate high quality 3D bounding boxes. To facilitate future research inobject detection for NeRF, we built a new benchmark dataset which consists ofboth synthetic and real-world data with careful labeling and clean up. Code anddataset are available at https://github.com/lyclyc52/NeRF_RPN.

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