Summary of Learning Shared Rgb-d Fields: Unified Self-supervised Pre-training For Label-efficient Lidar-camera 3d Perception, by Xiaohao Xu et al.
Learning Shared RGB-D Fields: Unified Self-supervised Pre-training for Label-efficient LiDAR-Camera 3D Perception
by Xiaohao Xu, Ye Li, Tianyi Zhang, Jinrong Yang, Matthew Johnson-Roberson, Xiaonan Huang
First submitted to arxiv on: 28 May 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 This paper addresses the challenge of constructing large-scale labeled datasets for training multi-modal perception models in autonomous driving. Existing self-supervised pretraining strategies often employ distinct approaches for each modality, which can be inefficient and lead to suboptimal results. To address this issue, the authors propose a unified pretraining strategy called NeRF-Supervised Masked Auto Encoder (NS-MAE) that optimizes all modalities through a shared formulation. NS-MAE leverages the ability of NeRF to encode both appearance and geometry, enabling efficient masked reconstruction of multi-modal data. The method extracts embeddings from corrupted LiDAR point clouds and images, conditions them on view directions and locations, and then renders them into multi-modal feature maps for 3D driving perception tasks. The authors demonstrate the superior transferability of NS-MAE across various 3D perception tasks under different fine-tuning settings, outperforming prior state-of-the-art pre-training methods that employ separate strategies for each modality. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about how to train computers to understand and make decisions about what’s happening in the world around them. They want to teach these computers to use data from cameras, sensors, and other sources to drive cars safely and efficiently. The authors found a way to do this by using a special kind of training that doesn’t need labeled examples (labels are like instructions on how to do something). This method is called NS-MAE, and it’s really good at taking in messy data and making sense of it. It works by looking at the data from different angles and then putting together what it sees into a complete picture. The authors tested this method and found that it does better than other methods at doing certain tasks. |
Keywords
» Artificial intelligence » Encoder » Fine tuning » Mae » Multi modal » Pretraining » Self supervised » Supervised » Transferability
