用概率高斯叠加提升3D占位预测效率与精度
GaussianFormer-2: Probabilistic Gaussian Superposition for Efficient 3D Occupancy Prediction
- 将每个高斯看作邻域占位概率，通过概率乘法融合整体几何
- 在nuScenes和KITTI-360上达到最优性能，推理效率高
- 基于像素对齐分布初始化，更好定位非空区域
3D语义占位预测是实现鲁棒视觉中心自动驾驶的关键任务，需精确刻画场景的细粒度几何与语义。现有方法多采用密集网格表示，忽略驾驶场景的空间稀疏性。尽管3D语义高斯作为以物体为中心的稀疏替代方案，但多数高斯仍用于描述空区域，效率低下。为此，本文提出概率高斯叠加模型，将每个高斯解释为邻域占位的概率分布，并遵循概率乘法规则推导整体几何结构。同时，采用精确高斯混合模型计算语义，避免高斯间的无效重叠。为高效初始化非空区域的高斯，设计基于分布的初始化模块，学习像素对齐的占位分布而非表面深度。在nuScenes和KITTI-360数据集上进行了大量实验，GaussianFormer-2 实现了先进性能并具备高效率。代码已开源：https://github.com/huang-yh/GaussianFormer。
原文摘要 · Abstract (English)
3D semantic occupancy prediction is an important task for robust vision-centric autonomous driving, which predicts fine-grained geometry and semantics of the surrounding scene. Most existing methods leverage dense grid-based scene representations, overlooking the spatial sparsity of the driving scenes. Although 3D semantic Gaussian serves as an object-centric sparse alternative, most of the Gaussians still describe the empty region with low efficiency. To address this, we propose a probabilistic Gaussian superposition model which interprets each Gaussian as a probability distribution of its neighborhood being occupied and conforms to probabilistic multiplication to derive the overall geometry. Furthermore, we adopt the exact Gaussian mixture model for semantics calculation to avoid unnecessary overlapping of Gaussians. To effectively initialize Gaussians in non-empty region, we design a distribution-based initialization module which learns the pixel-aligned occupancy distribution instead of the depth of surfaces. We conduct extensive experiments on nuScenes and KITTI-360 datasets and our GaussianFormer-2 achieves state-of-the-art performance with high efficiency. Code: https://github.com/huang-yh/GaussianFormer.
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