Summary of Embodiedocc: Embodied 3d Occupancy Prediction For Vision-based Online Scene Understanding, by Yuqi Wu et al.
EmbodiedOcc: Embodied 3D Occupancy Prediction for Vision-based Online Scene Understanding
by Yuqi Wu, Wenzhao Zheng, Sicheng Zuo, Yuanhui Huang, Jie Zhou, Jiwen Lu
First submitted to arxiv on: 5 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a novel approach to 3D occupancy prediction, focusing on embodied agents that gradually perceive the scene through progressive exploration. The authors formulate an embodied 3D occupancy prediction task and introduce a Gaussian-based EmbodiedOcc framework to tackle this challenge. They initialize the global scene with uniform 3D semantic Gaussians and progressively update local regions observed by the agent. This is achieved by extracting semantic and structural features from the observed image and incorporating them via deformable cross-attention to refine regional Gaussians. The authors employ Gaussian-to-voxel splatting to obtain the global 3D occupancy from the updated Gaussians. Experiments demonstrate that EmbodiedOcc outperforms existing local prediction methods, achieving high accuracy and strong expandability. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper explores how a robot or agent can learn about its surroundings by gradually perceiving the scene through exploration. The authors create a new task for predicting what’s in the 3D space around the agent as it moves and sees different parts of the scene. They use a special kind of statistical model called Gaussian-based EmbodiedOcc to do this prediction. This approach is useful because it allows the agent to learn about its environment by gradually refining its understanding through observation. The authors test their method using a dataset with labeled 3D scenes and show that it works well. |
Keywords
» Artificial intelligence » Cross attention » Statistical model
