用3D高斯与边缘先验提升3D全景占据的几何精度和边界感知。
HyGE-Occ: Hybrid View-Transformation with 3D Gaussian and Edge Priors for 3D Panoptic Occupancy Prediction
- 融合连续高斯与离散深度箱，增强鸟瞰图特征的几何一致性。
- 通过提取边缘图辅助学习，提升实例边界的准确识别。
- 在Occ3D-nuScenes上表现优于现有方法，适合自动驾驶场景理解。
3D全景占据预测旨在通过预测三维空间中每个占据区域的语义类别和实例身份，重建稠密体素场景地图。实现这种细粒度三维理解需要精确的几何推理和复杂环境中的空间一致性场景表示。然而，现有方法常难以保持精确几何结构并捕捉关键3D实例的空间范围，影响鲁棒的全景分割。为此，我们提出HyGE-Occ，一种新颖框架，利用结合3D高斯与边缘先验的混合视角转换分支，提升3D全景占据预测中的几何一致性和边界感知能力。HyGE-Occ采用混合视角转换分支，融合基于连续高斯的深度表示与离散深度箱形式，生成具有更好几何一致性和结构连贯性的鸟瞰图（BEV）特征。同时，从BEV特征中提取边缘图，并作为辅助信息以学习边缘线索。在Occ3D-nuScenes数据集上的大量实验表明，HyGE-Occ在3D几何推理方面优于现有方法。
原文摘要 · Abstract (English)
3D Panoptic Occupancy Prediction aims to reconstruct a dense volumetric scene map by predicting the semantic class and instance identity of every occupied region in 3D space. Achieving such fine-grained 3D understanding requires precise geometric reasoning and spatially consistent scene representation across complex environments. However, existing approaches often struggle to maintain precise geometry and capture the precise spatial range of 3D instances critical for robust panoptic separation. To overcome these limitations, we introduce HyGE-Occ, a novel framework that leverages a hybrid view-transformation branch with 3D Gaussian and edge priors to enhance both geometric consistency and boundary awareness in 3D panoptic occupancy prediction. HyGE-Occ employs a hybrid view-transformation branch that fuses a continuous Gaussian-based depth representation with a discretized depth-bin formulation, producing BEV features with improved geometric consistency and structural coherence. In parallel, we extract edge maps from BEV features and use them as auxiliary information to learn edge cues. In our extensive experiments on the Occ3D-nuScenes dataset, HyGE-Occ outperforms existing work, demonstrating superior 3D geometric reasoning.
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