Summary of Wildocc: a Benchmark For Off-road 3d Semantic Occupancy Prediction, by Heng Zhai et al.
WildOcc: A Benchmark for Off-Road 3D Semantic Occupancy Prediction
by Heng Zhai, Jilin Mei, Chen Min, Liang Chen, Fangzhou Zhao, Yu Hu
First submitted to arxiv on: 21 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Robotics (cs.RO)
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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 introduces WildOcc, the first benchmark for off-road 3D semantic occupancy prediction tasks, providing dense occupancy annotations for such environments. It presents a ground truth generation pipeline that employs coarse-to-fine reconstruction and achieves realistic results. The framework fuses spatio-temporal information from multi-frame images and point clouds at the voxel level, and incorporates cross-modality distillation to transfer geometric knowledge from point clouds to image features. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary WildOcc is a new benchmark for predicting 3D semantic occupancy in off-road environments. This helps autonomous vehicles better understand the world around them. The researchers created a way to generate accurate ground truth data, which is important for training and testing models. They also developed a special kind of AI model that combines information from images and point clouds to make more accurate predictions. |
Keywords
» Artificial intelligence » Distillation
