Vision-based semantic occupancy and flow prediction plays a crucial role in providing spatiotemporal cues for real-world tasks, such as autonomous driving. Existing methods prioritize higher accuracy to cater to the demands of these tasks. In this work, we strive to improve performance by introducing a series of targeted improvements for 3D semantic occupancy prediction and flow estimation. First, we introduce an occlusion-aware adaptive lifting mechanism with a depth denoising technique to improve the robustness of 2D-to-3D feature transformation and reduce the reliance on depth priors. Second, we strengthen the semantic consistency between 3D features and their original 2D modalities by utilizing shared semantic prototypes to jointly constrain both 2D and 3D features. This is complemented by confidence- and category-based sampling strategies to tackle long-tail challenges in 3D space. To alleviate the feature encoding burden in the joint prediction of semantics and flow, we propose a BEV cost volume-based prediction method that links flow and semantic features through a cost volume and employs a classification-regression supervision scheme to address the varying flow scales in dynamic scenes. Our purely convolutional architecture framework, named ALOcc, achieves an optimal tradeoff between speed and accuracy achieving state-of-the-art results on multiple benchmarks. On Occ3D and training without the camera visible mask, our ALOcc achieves an absolute gain of 2.5\% in terms of RayIoU while operating at a comparable speed compared to the state-of-the-art, using the same input size (256$\times$704) and ResNet-50 backbone. Our method also achieves 2nd place in the CVPR24 Occupancy and Flow Prediction Competition.

本研究解决了现有视觉基础的语义占用和流预测在准确性上的不足，通过引入了一种考虑遮挡的自适应升维机制和深度去噪技术，增强了2D与3D特征的转换。我们的主要发现是，提出的ALOcc框架在多个基准测试中达到了速度与准确性的最佳平衡，并在CVPR24占用与流预测比赛中获得第二名。

ALOcc：自适应升维的3D语义占用与成本体积流预测