FAST3D: Flow-Aware Self-Training for 3D Object Detectors

• URL: http://arxiv.org/abs/2110.09355v1
• Date: Mon, 18 Oct 2021 14:32:05 GMT
• Title: FAST3D: Flow-Aware Self-Training for 3D Object Detectors
• Authors: Christian Fruhwirth-Reisinger, Michael Opitz, Horst Possegger, Horst Bischof
• Abstract summary: State-of-the-art self-training approaches mostly ignore the temporal nature of autonomous driving data. We propose a flow-aware self-training method that enables unsupervised domain adaptation for 3D object detectors on continuous LiDAR point clouds. Our results show a significant improvement over the state-of-the-art, without any prior target domain knowledge.
• Score: 12.511087244102036
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: In the field of autonomous driving, self-training is widely applied to mitigate distribution shifts in LiDAR-based 3D object detectors. This eliminates the need for expensive, high-quality labels whenever the environment changes (e.g., geographic location, sensor setup, weather condition). State-of-the-art self-training approaches, however, mostly ignore the temporal nature of autonomous driving data. To address this issue, we propose a flow-aware self-training method that enables unsupervised domain adaptation for 3D object detectors on continuous LiDAR point clouds. In order to get reliable pseudo-labels, we leverage scene flow to propagate detections through time. In particular, we introduce a flow-based multi-target tracker, that exploits flow consistency to filter and refine resulting tracks. The emerged precise pseudo-labels then serve as a basis for model re-training. Starting with a pre-trained KITTI model, we conduct experiments on the challenging Waymo Open Dataset to demonstrate the effectiveness of our approach. Without any prior target domain knowledge, our results show a significant improvement over the state-of-the-art.

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