  Accurate perception of the dynamic environment is a fundamental task for
autonomous driving and robot systems. This paper introduces Let Occ Flow, the
first self-supervised work for joint 3D occupancy and occupancy flow prediction
using only camera inputs, eliminating the need for 3D annotations. Utilizing
TPV for unified scene representation and deformable attention layers for
feature aggregation, our approach incorporates a backward-forward temporal
attention module to capture dynamic object dependencies, followed by a 3D
refine module for fine-gained volumetric representation. Besides, our method
extends differentiable rendering to 3D volumetric flow fields, leveraging
zero-shot 2D segmentation and optical flow cues for dynamic decomposition and
motion optimization. Extensive experiments on nuScenes and KITTI datasets
demonstrate the competitive performance of our approach over prior
state-of-the-art methods.
