Abstract
Estimating 3D occupancy and motion at the vehicle's surroundings is essential for autonomous driving, enabling situational awareness in dynamic environments. Existing approaches jointly learn geometry and motion but rely on expensive 3D occupancy and flow annotations, velocity labels from bounding boxes, or pretrained optical flow models. We propose a self-supervised method for 3D occupancy flow estimation that eliminates the need for human-produced annotations or external flow supervision. Our method disentangles the scene into separate static and dynamic signed distance fields and learns motion implicitly through temporal aggregation. Additionally, we introduce a strong self-supervised flow cue derived from features' cosine similarities. We demonstrate the efficacy of our 3D occupancy flow method on SemanticKITTI, KITTI-MOT, and nuScenes.
Abstract (translated)
在自动驾驶中，估算车辆周围环境的三维占用情况和运动状态对于实现情境感知至关重要，尤其是在动态环境中。现有的方法能够同时学习几何形状和运动，但依赖于昂贵的3D占用标注、来自边界框的速度标签或预训练的光流模型。我们提出了一种无需人工生成注释或外部流动监督的自我监督方法来估算三维占用流。该方法将场景分解为独立的静态和动态有符号距离字段，并通过时间聚合隐式学习运动。此外，我们引入了一个强大的自我监督流动线索，来源于特征余弦相似度。我们在SemanticKITTI、KITTI-MOT和nuScenes数据集上展示了我们的3D占用流估算方法的有效性。
URL
https://arxiv.org/abs/2602.23894
