In this technical report, we present our solution for the Vision-Centric 3D
Occupancy and Flow Prediction track in the nuScenes Open-Occ Dataset Challenge
at CVPR 2024. Our innovative approach involves a dual-stage framework that
enhances 3D occupancy and flow predictions by incorporating adaptive forward
view transformation and flow modeling. Initially, we independently train the
occupancy model, followed by flow prediction using sequential frame
integration. Our method combines regression with classification to address
scale variations in different scenes, and leverages predicted flow to warp
current voxel features to future frames, guided by future frame ground truth.
Experimental results on the nuScenes dataset demonstrate significant
improvements in accuracy and robustness, showcasing the effectiveness of our
approach in real-world scenarios. Our single model based on Swin-Base ranks
second on the public leaderboard, validating the potential of our method in
advancing autonomous car perception systems.

通过采用自适应前视转换和流建模的双阶段框架，我们提出了一种创新的方法，以增强 3D 占用和流的预测能力。我们首先独立训练占用模型，然后使用连续帧集成进行流预测。我们的方法将回归与分类相结合，以解决不同场景中的尺度变化，并利用预测的流将当前体素特征扭曲到未来帧，由未来帧的真值引导。在 nuScenes 数据集上的实验结果表明，我们的方法在准确性和鲁棒性方面取得了显著的改进，展示了我们方法在真实环境下的有效性。基于 Swin-Base 的单一模型在公共排行榜上排名第二，验证了我们方法在推进自动驾驶车辆感知系统方面的潜力。