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LiDAR based 3D object detectors typically need a large amount ofdetailed-labeled point cloud data for training, but these detailed labels arecommonly expensive to acquire. In this paper, we propose a manual-label free 3Ddetection algorithm that leverages the CARLA simulator to generate a largeamount of self-labeled training samples and introduces a novel Domain AdaptiveVoxelNet (DA-VoxelNet) that can cross the distribution gap from the syntheticdata to the real scenario. The self-labeled training samples are generated by aset of high quality 3D models embedded in a CARLA simulator and a proposedLiDAR-guided sampling algorithm. Then a DA-VoxelNet that integrates both asample-level DA module and an anchor-level DA module is proposed to enable thedetector trained by the synthetic data to adapt to real scenario. Experimentalresults show that the proposed unsupervised DA 3D detector on KITTI evaluationset can achieve 76.66% and 56.64% mAP on BEV mode and 3D mode respectively. Theresults reveal a promising perspective of training a LIDAR-based 3D detectorwithout any hand-tagged label.

Manual-Label Free 3D Detection via An Open-Source Simulator