Abstract
Understanding motion in dynamic environments is critical for autonomous driving, thereby motivating research on class-agnostic motion prediction. In this work, we investigate weakly and self-supervised class-agnostic motion prediction from LiDAR point clouds. Outdoor scenes typically consist of mobile foregrounds and static backgrounds, allowing motion understanding to be associated with scene parsing. Based on this observation, we propose a novel weakly supervised paradigm that replaces motion annotations with fully or partially annotated (1%, 0.1%) foreground/background masks for supervision. To this end, we develop a weakly supervised approach utilizing foreground/background cues to guide the self-supervised learning of motion prediction models. Since foreground motion generally occurs in non-ground regions, non-ground/ground masks can serve as an alternative to foreground/background masks, further reducing annotation effort. Leveraging non-ground/ground cues, we propose two additional approaches: a weakly supervised method requiring fewer (0.01%) foreground/background annotations, and a self-supervised method without annotations. Furthermore, we design a Robust Consistency-aware Chamfer Distance loss that incorporates multi-frame information and robust penalty functions to suppress outliers in self-supervised learning. Experiments show that our weakly and self-supervised models outperform existing self-supervised counterparts, and our weakly supervised models even rival some supervised ones. This demonstrates that our approaches effectively balance annotation effort and performance.
Abstract (translated)
理解动态环境中物体的运动对于自动驾驶至关重要，从而推动了无类别限制（class-agnostic）运动预测的研究。在这项工作中，我们研究了基于激光雷达点云的弱监督和自监督无类别限制运动预测方法。室外场景通常由移动前景和静态背景组成，这使得对运动的理解可以与场景解析相关联。基于这一观察，我们提出了一种新颖的弱监督范式，用完全或部分标注（1%，0.1%）的前景/背景掩码来替代运动标注以进行监督。 为此，我们开发了一种利用前景/背景线索指导自监督学习的方法，用于运动预测模型。鉴于前景运动通常发生在非地面区域中，非地面/地面掩模可以作为前景/背景掩模的替代品使用，进一步减少注释工作量。基于非地面/地面提示，我们提出了两种额外方法：一种弱监督方法需要更少（0.01%）的前景/背景标注，以及一种无需任何标注的自监督方法。 此外，为了增强自监督学习中的鲁棒性和一致性，我们设计了一种新颖的Robust Consistency-aware Chamfer Distance损失函数。该函数融合了多帧信息，并使用鲁棒惩罚函数来抑制异常值。 实验结果表明，我们的弱监督和自监督模型优于现有的自监督方法，甚至在某些情况下，我们的弱监督模型与一些有监督的方法性能相当。这证明了我们提出的方法能够在标注工作量和性能之间找到有效的平衡点。
URL
https://arxiv.org/abs/2509.13116
