Summary of Ssl-nbv: a Self-supervised-learning-based Next-best-view Algorithm For Efficient 3d Plant Reconstruction by a Robot, By Jianchao Ci et al.
SSL-NBV: A Self-Supervised-Learning-Based Next-Best-View algorithm for Efficient 3D Plant Reconstruction by a Robot
by Jianchao Ci, Eldert J. van Henten, Xin Wang, Akshay K. Burusa, Gert Kootstra
First submitted to arxiv on: 2 Oct 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 A deep-learning-based Next-Best-View (DL-NBV) method is proposed to efficiently reconstruct 3D plants by iteratively selecting new viewpoints that maximize information gain. The current DL-NBV methods require extensive training using ground-truth plant models, making them impractical for real-world plants. To address this limitation, a self-supervised learning-based NBV method (SSL-NBV) is developed that uses a deep neural network to predict the IG for candidate viewpoints. The proposed method allows the robot to gather its own training data during task execution and employs weakly-supervised learning and experience replay for efficient online learning. Comprehensive evaluations were conducted in simulation and real-world environments using cross-validation, showing that SSL-NBV required fewer views for plant reconstruction than non-NBV methods and was over 800 times faster than a voxel-based method. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of researchers developed a new way to create detailed pictures of plants from different angles. They used computers to teach themselves which viewpoints would give the best results, without needing any special training data. This is important because it allows robots or other machines to learn how to reconstruct 3D images of plants on their own, even in changing environments. |
Keywords
» Artificial intelligence » Deep learning » Neural network » Online learning » Self supervised » Supervised
