# RoboOcc: Enhancing the Geometric and Semantic Scene Understanding for Robots

arXiv: https://arxiv.org/abs/2504.14604 · HTML: https://mlsanity.com/paper/2504.14604/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Apr 2025.

## Built on by (most cited first, 15 of 15)

- [SplatSSC: Decoupled Depth-Guided Gaussian Splatting for Semantic Scene Completion](https://mlsanity.com/paper/2508.02261/) (2025)
- [Occupancy World Model for Robots](https://mlsanity.com/paper/2505.05512/) (2025)
- [OneOcc: Semantic Occupancy Prediction for Legged Robots with a Single Panoramic Camera](https://mlsanity.com/paper/2511.03571/) (2025)
- [Humanoid Occupancy: Enabling A Generalized Multimodal Occupancy Perception System on Humanoid Robots](https://mlsanity.com/paper/2507.20217/) (2025)
- [Monocular Open Vocabulary Occupancy Prediction for Indoor Scenes](https://mlsanity.com/paper/2602.22667/) (2026)
- [TGSFormer: Scalable Temporal Gaussian Splatting for Embodied Semantic Scene Completion](https://mlsanity.com/paper/2512.00300/) (2025)
- [O3N: Omnidirectional Open-Vocabulary Occupancy Prediction for Embodied Intelligent Robotics](https://mlsanity.com/paper/2603.12144/) (2026)
- [FreeOcc: Training-Free Embodied Open-Vocabulary Occupancy Prediction](https://mlsanity.com/paper/2604.28115/) (2026)
- [YouTube-Occ: Learning Indoor 3D Semantic Occupancy Prediction from YouTube Videos](https://mlsanity.com/paper/2506.18266/) (2025)
- [VEOcc: Voxel-Centric Online Semantic Occupancy Prediction For Embodied Scene Understanding](https://mlsanity.com/paper/2605.25059/) (2026)
- [Bridging 3D Gaussians and Semantic Occupancy for Comprehensive Open-Vocabulary Scene Understanding from Unposed Images](https://mlsanity.com/paper/2607.01633/) (2026)
- [RobotPan: A 360^circ Surround-View Robotic Vision System for Embodied Perception](https://mlsanity.com/paper/2604.13476/) (2026)
- [GPOcc++: Unified Sparse Gaussian Occupancy Prediction with Visual Geometry Priors](https://mlsanity.com/paper/2607.13481/) (2026)
- [OccAnyScene: Towards Unified Indoor-Outdoor 3D Occupancy Prediction](https://mlsanity.com/paper/2608.08696/) (2026)
- [AdaOcc: Adaptive 3D Occupancy Prediction for Embodied Tasks](https://mlsanity.com/paper/2609.38864/) (2026)

## Abstract (arXiv)

3D occupancy prediction enables the robots to obtain spatial fine-grained geometry and semantics of the surrounding scene, and has become an essential task for embodied perception. Existing methods based on 3D Gaussians instead of dense voxels do not effectively exploit the geometry and opacity properties of Gaussians, which limits the network's estimation of complex environments and also limits the description of the scene by 3D Gaussians. In this paper, we propose a 3D occupancy prediction method which enhances the geometric and semantic scene understanding for robots, dubbed RoboOcc. It utilizes the Opacity-guided Self-Encoder (OSE) to alleviate the semantic ambiguity of overlapping Gaussians and the Geometry-aware Cross-Encoder (GCE) to accomplish the fine-grained geometric modeling of the surrounding scene. We conduct extensive experiments on Occ-ScanNet and EmbodiedOcc-ScanNet datasets, and our RoboOcc achieves state-of the-art performance in both local and global camera settings. Further, in ablation studies of Gaussian parameters, the proposed RoboOcc outperforms the state-of-the-art methods by a large margin of (8.47, 6.27) in IoU and mIoU metric, respectively. The codes will be released soon.