EmbodiedOcc: Embodied 3D Occupancy Prediction for Vision-based Online Scene Understanding

TL;DR

This paper introduces EmbodiedOcc, a novel framework for online 3D occupancy prediction that enables embodied agents to incrementally understand scenes through exploration, outperforming existing offline methods.

Contribution

The paper proposes a Gaussian-based embodied 3D occupancy prediction method that updates scene understanding through local observations, a new benchmark, and demonstrates superior performance.

Findings

Outperforms existing methods significantly.

Achieves high accuracy and efficiency in scene understanding.

Provides a new benchmark for embodied 3D occupancy prediction.

Abstract

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 that demand 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. Our EmbodiedOcc outperforms existing methods by a large margin and accomplishes the embodied occupancy prediction with high accuracy and efficiency. Code: https://github.com/YkiWu/EmbodiedOcc.