This paper introduces the Masked Voxel Jigsaw and Reconstruction (MV-JAR) method for LiDAR-based self-supervised pre-training and a carefully designed data-efficient 3D object detection benchmark on the Waymo dataset. Inspired by the scene-voxel-point hierarchy in downstream 3D object detectors, we design masking and reconstruction strategies accounting for voxel distributions in the scene and local point distributions within the voxel. We employ a Reversed-Furthest-Voxel-Sampling strategy to address the uneven distribution of LiDAR points and propose MV-JAR, which combines two techniques for modeling the aforementioned distributions, resulting in superior performance. Our experiments reveal limitations in previous data-efficient experiments, which uniformly sample fine-tuning splits with varying data proportions from each LiDAR sequence, leading to similar data diversity across splits. To address this, we propose a new benchmark that samples scene sequences for diverse fine-tuning splits, ensuring adequate model convergence and providing a more accurate evaluation of pre-training methods. Experiments on our Waymo benchmark and the KITTI dataset demonstrate that MV-JAR consistently and significantly improves 3D detection performance across various data scales, achieving up to a 6.3% increase in mAPH compared to training from scratch. Codes and the benchmark will be available at https://github.com/SmartBot-PJLab/MV-JAR .

翻译：本文提出了掩码体素拼图与重建（MV-JAR）方法，用于激光雷达自监督预训练，并在Waymo数据集上精心设计了一个数据高效的3D目标检测基准。受下游3D目标检测器中场景-体素-点层次结构的启发，我们设计了考虑场景中体素分布以及体素内局部点分布的掩码与重建策略。采用反向最远体素采样策略解决激光雷达点分布不均匀的问题，并提出MV-JAR，该方法结合两种技术对上述分布进行建模，从而实现了优越的性能。实验揭示了以往数据高效实验的局限性：这些实验从每个激光雷达序列中按不同数据比例均匀采样微调子集，导致不同子集间的数据多样性相似。为解决这一问题，我们提出一个新基准，通过采样场景序列来构建多样化的微调子集，确保模型充分收敛，并更准确地评估预训练方法。在Waymo基准和KITTI数据集上的实验表明，MV-JAR在不同数据规模下均能持续显著提升3D检测性能，与从头训练相比，mAPH最高提升6.3%。代码和基准将发布在 https://github.com/SmartBot-PJLab/MV-JAR 。