Boosting 3D Object Detection by Simulating Multimodality on Point Clouds

Wu Zheng; Mingxuan Hong; Li Jiang; Chi-Wing Fu

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arXiv · 2206.14971

Boosting 3D Object Detection by Simulating Multimodality on Point Clouds

Wu Zheng·Mingxuan Hong·Li Jiang·Chi-Wing Fu

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Abstract

This paper presents a new approach to boost a single-modality (LiDAR) 3D object detector by teaching it to simulate features and responses that follow a multi-modality (LiDAR-image) detector. The approach needs LiDAR-image data only when training the single-modality detector, and once well-trained, it only needs LiDAR data at inference. We design a novel framework to realize the approach: response distillation to focus on the crucial response samples and avoid the background samples; sparse-voxel distillation to learn voxel semantics and relations from the estimated crucial voxels; a fine-grained voxel-to-point distillation to better attend to features of small and distant objects; and instance distillation to further enhance the deep-feature consistency. Experimental results on the nuScenes dataset show that our approach outperforms all SOTA LiDAR-only 3D detectors and even surpasses the baseline LiDAR-image detector on the key NDS metric, filling 72% mAP gap between the single- and multi-modality detectors.

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BibTeX RIS

Wu Zheng, Mingxuan Hong, Li Jiang, Chi-Wing Fu. 2022-06-30. Boosting 3D Object Detection by Simulating Multimodality on Point Clouds. https://arxiv.org/abs/2206.14971

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