用T-原始体建模3D占位，融合相机与激光雷达提升感知精度
TFusionOcc: T-Primitive Based Object-Centric Multi-Sensor Fusion Framework for 3D Occupancy Prediction
- 基于t分布设计T-原始体，支持复杂非凸结构建模
- 在nuScenes上达到最新最优性能，腐蚀场景下仍具强鲁棒性
- 适合自动驾驶中需要精细环境理解的场景
3D语义占位预测使自动驾驶车辆能够感知细粒度的几何与语义场景结构，以实现安全导航与决策。现有方法主要依赖体素表示（冗余计算空区域）或基于高斯原始体的对象中心建模（难以刻画复杂、非凸、非对称结构）。本文提出TFusionOcc，一种基于T-原始体的对象中心多传感器融合框架。我们引入一族基于学生t分布的T-原始体，包括普通T-原始体、T-超二次曲面以及带逆向变形的可变形T-超二次曲面，其中可变形T-超二次曲面作为核心几何增强单元。进一步构建基于学生t分布与T混合模型（TMM）的统一概率公式，联合建模占位与语义，并设计紧密耦合的多阶段融合架构，有效整合摄像头与激光雷达信息。在nuScenes数据集上的大量实验显示达到最先进性能，nuScenes-C上的额外评估表明其在多数损坏场景下仍具强鲁棒性。代码将公开于：https://github.com/DanielMing123/TFusionOcc
原文摘要 · Abstract (English)
The prediction of 3D semantic occupancy enables autonomous vehicles (AVs) to perceive the fine-grained geometric and semantic scene structure for safe navigation and decision-making. Existing methods mainly rely on either voxel-based representations, which incur redundant computation over empty regions, or on object-centric Gaussian primitives, which are limited in modeling complex, non-convex, and asymmetric structures. In this paper, we present TFusionOcc, a T-primitive-based object-centric multi-sensor fusion framework for 3D semantic occupancy prediction. Specifically, we introduce a family of Students t-distribution-based T-primitives, including the plain T-primitive, T-Superquadric, and deformable T-Superquadric with inverse warping, where the deformable T-Superquadric serves as the key geometry-enhancing primitive. We further develop a unified probabilistic formulation based on the Students t-distribution and the T-mixture model (TMM) to jointly model occupancy and semantics, and design a tightly coupled multi-stage fusion architecture to effectively integrate camera and LiDAR cues. Extensive experiments on nuScenes show state-of-the-art performance, while additional evaluations on nuScenes-C demonstrate strong robustness under most corruption scenarios. The code will be available at: https://github.com/DanielMing123/TFusionOcc
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