Improving 3D Object Detection in Neural Radiance Fields With Channel Attention

Abstract

In recent years, 3D object detection using neural radiance fields (NeRF) has advanced significantly, yet challenges remain in effectively utilising the density field. Current methods often treat NeRF as a geometry learning tool or rely on volume rendering, neglecting the density field's potential and feature dependencies. To address this, we propose NeRF-C3D, a novel framework incorporating a multi-scale feature fusion module with channel attention (MFCA). MFCA leverages channel attention to model feature dependencies, dynamically adjusting channel weights during fusion to enhance important features and suppress redundancy. This optimises density field representation and improves feature discriminability. Experiments on 3D-FRONT, Hypersim, and ScanNet demonstrate NeRF-C3D's superior performance validating MFCA's effectiveness in capturing feature relationships and showcasing its innovation in NeRF-based 3D detection.