Summary of Sense Less, Generate More: Pre-training Lidar Perception with Masked Autoencoders For Ultra-efficient 3d Sensing, by Sina Tayebati et al.
Sense Less, Generate More: Pre-training LiDAR Perception with Masked Autoencoders for Ultra-Efficient 3D Sensing
by Sina Tayebati, Theja Tulabandhula, Amit R. Trivedi
First submitted to arxiv on: 12 Jun 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 The paper proposes a novel approach to LiDAR perception dataflow that generates rather than senses parts of the environment, allowing for low-power robotics and autonomous navigation. The method, called radially masked autoencoding (R-MAE), pre-trains a generative model on predictable or low-consequence areas, reducing sensing energy while improving prediction accuracy. Evaluations on Waymo, nuScenes, and KITTI datasets show significant improvements in detection tasks, including 5% average precision increase and 4% accuracy improvement. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes an innovative way to make LiDAR sensors more efficient by only sensing what’s really important. This is done by training a model to predict parts of the environment that are easy or not very important. The results show that this method works well, improving detection tasks and reducing energy consumption. For example, on the Waymo dataset, it gets better at detecting small objects. |
Keywords
» Artificial intelligence » Generative model » Mae » Precision
