3D occupancy prediction based on multi-sensor fusion, crucial for a reliable
autonomous driving system, enables fine-grained understanding of 3D scenes.
Previous fusion-based 3D occupancy predictions relied on depth estimation for
processing 2D image features. However, depth estimation is an ill-posed
problem, hindering the accuracy and robustness of these methods. Furthermore,
fine-grained occupancy prediction demands extensive computational resources. We
introduce OccFusion, a multi-modal fusion method free from depth estimation,
and a corresponding point cloud sampling algorithm for dense integration of
image features. Building on this, we propose an active training method and an
active coarse to fine pipeline, enabling the model to adaptively learn more
from complex samples and optimize predictions specifically for challenging
areas such as small or overlapping objects. The active methods we propose can
be naturally extended to any occupancy prediction model. Experiments on the
OpenOccupancy benchmark show our method surpasses existing state-of-the-art
(SOTA) multi-modal methods in IoU across all categories. Additionally, our
model is more efficient during both the training and inference phases,
requiring far fewer computational resources. Comprehensive ablation studies
demonstrate the effectiveness of our proposed techniques.

基于多传感器融合的 3D 占用预测方法 OccFusion，使用无需深度估计的多模态融合以及相应的点云采样算法，通过主动训练方法和主动粗到细流程，提高了复杂场景中预测的准确性和鲁棒性，有效优化了计算资源需求。在开放占用评测中，该方法超过了现有基于多模态的最先进方法，并且训练和推理阶段更加高效。细致的消融研究证明了我们提出的技术的有效性。