3D occupancy prediction provides a comprehensive description of the surrounding scenes and has become an essential task for 3D perception. Most existing methods focus on offline perception from one or a few views and cannot be applied to embodied agents which demands to gradually perceive the scene through progressive embodied exploration. In this paper, we formulate an embodied 3D occupancy prediction task to target this practical scenario and propose a Gaussian-based EmbodiedOcc framework to accomplish it. We initialize the global scene with uniform 3D semantic Gaussians and progressively update local regions observed by the embodied agent. For each update, we extract semantic and structural features from the observed image and efficiently incorporate them via deformable cross-attention to refine the regional Gaussians. Finally, we employ Gaussian-to-voxel splatting to obtain the global 3D occupancy from the updated 3D Gaussians. Our EmbodiedOcc assumes an unknown (i.e., uniformly distributed) environment and maintains an explicit global memory of it with 3D Gaussians. It gradually gains knowledge through the local refinement of regional Gaussians, which is consistent with how humans understand new scenes through embodied exploration. We reorganize an EmbodiedOcc-ScanNet benchmark based on local annotations to facilitate the evaluation of the embodied 3D occupancy prediction task. Experiments demonstrate that our EmbodiedOcc outperforms existing local prediction methods and accomplishes the embodied occupancy prediction with high accuracy and strong expandability. Code: https://github.com/YkiWu/EmbodiedOcc.

翻译：三维占据预测为周围场景提供了全面描述，已成为三维感知的关键任务。现有方法大多聚焦于单视角或多视角的离线感知，无法应用于需要通过渐进式具身探索逐步感知场景的具身智能体。本文针对这一实际场景，提出具身三维占据预测任务，并构建基于高斯模型的EmbodiedOcc框架以实现该目标。我们使用均匀分布的三维语义高斯模型初始化全局场景，并逐步更新具身智能体观测到的局部区域。在每次更新中，我们从观测图像中提取语义与结构特征，通过可变形交叉注意力机制高效融合这些特征以优化区域高斯模型。最后，我们采用高斯-体素投射技术从更新后的三维高斯模型中获取全局三维占据信息。本方法假设环境先验未知（即均匀分布），通过三维高斯模型显式维护全局记忆，借助区域高斯模型的局部优化逐步获取场景知识，这与人类通过具身探索理解新场景的认知过程一致。基于局部标注数据重构的EmbodiedOcc-ScanNet基准数据集，为具身三维占据预测任务的评估提供了支持。实验表明，EmbodiedOcc在性能上超越现有局部预测方法，以高精度和强扩展性实现了具身占据预测。代码：https://github.com/YkiWu/EmbodiedOcc。