Abstract
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 this https URL .
Abstract (translated)
本文介绍了基于激光雷达的自我监督前训练方法Masked Voxel Jigsaw and Reconstruction(MV-JAR)，以及在谷歌自动驾驶数据集上精心设计的高效数据检测基准。受到后续3D物体检测器中场景点、像素级 hierarchy的灵感，我们设计Masked和 Reconstruction策略，考虑场景点和 voxel 点分布情况。我们采用Reversed-Furthest-Voxel-Sampling策略来解决 LiDAR 点分布不均的问题，并提出了MV-JAR，它结合了两种技术，用于建模上述分布，结果表现优异。我们的实验揭示了先前高效实验的局限性，这些实验uniformly samples fine-tuningsplits with varying data proportion from each LiDAR sequence，导致在不同split之间存在类似数据多样性的问题。为了解决这个问题，我们提出了一个新的基准，样本场景序列以不同的 fine-tuningsplit，确保模型充分收敛，并提供更精确的训练方法评估。在谷歌自动驾驶数据和KITTI数据集上的实验表明，MV-JAR consistently and significantly improves 3D检测性能，在各种数据尺度上实现6.3%的性能提升，相对于从头开始训练。代码和基准将在这httpsURL上提供。
URL
https://arxiv.org/abs/2303.13510
