Summary of Cvt-occ: Cost Volume Temporal Fusion For 3d Occupancy Prediction, by Zhangchen Ye et al.
CVT-Occ: Cost Volume Temporal Fusion for 3D Occupancy Prediction
by Zhangchen Ye, Tao Jiang, Chenfeng Xu, Yiming Li, Hang Zhao
First submitted to arxiv on: 20 Sep 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 CVT-Occ, a novel approach for vision-based 3D occupancy prediction, leverages temporal fusion through geometric correspondence of voxels over time to improve depth estimation. By sampling points along the line of sight and integrating features from historical frames, a cost volume feature map is constructed to refine current volume features. This data-driven method takes advantage of parallax cues and outperforms state-of-the-art methods on the Occ3D-Waymo dataset with minimal additional computational cost. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary CVT-Occ is a new way to predict what’s in a 3D space based on what you see from different angles. It uses clues from looking at things over time to figure out where objects are and what they look like. This helps make better predictions about what’s in the space, even when you only have one view. The team tested it with real data and showed that it works really well. |
Keywords
» Artificial intelligence » Depth estimation » Feature map
