用超二次曲面实现实时自监督语义占位估计，大幅降低内存占用
SuperQuadricOcc: Real-Time Self-Supervised Semantic Occupancy Estimation with Superquadric Volume Rendering
- 用超二次曲面建模场景，结合空间索引实现高效实时渲染
- 在Occ3D-nuScenes上达到最优RayIoU，推理速度达实时，内存消耗更少
- 适合追求低资源高效率的自动驾驶占位估计研究者使用
自监督语义占位估计因其无需人工标注而具有规模化应用潜力，特别适用于自动驾驶数据集。超二次曲面具备丰富的形状表达能力，非常适合此任务，但其在自监督设置中的应用受限于缺乏高效的渲染方法，难以将三维场景表示与二维训练伪标签对齐。为此，我们提出SuperQuadricOcc，首个基于超二次曲面的自监督占位模型。为克服渲染瓶颈，我们设计了一种实时体积渲染器，通过空间超二次曲面-体素索引机制，使每条射线仅查询邻近超二次曲面，显著降低内存和计算开销。相比以往基于高斯的方法，SuperQuadricOcc使用更少的几何基元，在Occ3D-nuScenes上实现最优的RayIoU，支持实时推理且内存占用大幅减少，并在下游占位预测与轨迹估计任务中优于高斯基线模型。
原文摘要 · Abstract (English)
Self-supervision for semantic occupancy estimation is appealing as it removes the labour-intensive manual annotation, thus allowing one to scale to larger autonomous driving datasets. Superquadrics offer an expressive shape family very suitable for this task, yet their deployment in a self-supervised setting has been hindered by the lack of efficient rendering methods to bridge the 3D scene representation with 2D training pseudo-labels. To address this, we introduce SuperQuadricOcc, the first self-supervised occupancy model to leverage superquadrics for scene representation. To overcome the rendering limitation, we propose a real-time volume renderer that preserves the fidelity of the superquadric shape during rendering. It relies on spatial superquadric-voxel indexing, restricting each ray sample to query only nearby superquadrics, thereby greatly reducing memory usage and computational cost. Using drastically fewer primitives than prior Gaussian-based methods, SuperQuadricOcc achieves state-of-the-art RayIoU on Occ3D-nuScenes with real-time inference and a substantially reduced memory footprint, while also outperforming Gaussian-based baselines on downstream occupancy forecasting and trajectory estimation.
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