Manual-Label Free 3D Detection via An Open-Source Simulator
Zhen Yang, Chi Zhang, Huiming Guo, Zhaoxiang Zhang

TL;DR
This paper introduces a novel method for 3D object detection using LiDAR data that eliminates the need for manual labels by leveraging synthetic data from a simulator and domain adaptation techniques.
Contribution
It presents a new domain adaptive VoxelNet that trains on synthetic data and adapts to real-world data, reducing labeling costs.
Findings
Achieves 76.66% mAP on KITTI BEV mode
Achieves 56.64% mAP on KITTI 3D mode
Demonstrates effective training without manual labels
Abstract
LiDAR based 3D object detectors typically need a large amount of detailed-labeled point cloud data for training, but these detailed labels are commonly expensive to acquire. In this paper, we propose a manual-label free 3D detection algorithm that leverages the CARLA simulator to generate a large amount of self-labeled training samples and introduces a novel Domain Adaptive VoxelNet (DA-VoxelNet) that can cross the distribution gap from the synthetic data to the real scenario. The self-labeled training samples are generated by a set of high quality 3D models embedded in a CARLA simulator and a proposed LiDAR-guided sampling algorithm. Then a DA-VoxelNet that integrates both a sample-level DA module and an anchor-level DA module is proposed to enable the detector trained by the synthetic data to adapt to real scenario. Experimental results show that the proposed unsupervised DA 3D…
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Taxonomy
TopicsAdvanced Neural Network Applications · Robotics and Sensor-Based Localization · 3D Surveying and Cultural Heritage
MethodsEntropy Regularization · Proximal Policy Optimization · CARLA: An Open Urban Driving Simulator
