Summary of Tesgnn: Temporal Equivariant Scene Graph Neural Networks For Efficient and Robust Multi-view 3d Scene Understanding, by Quang P. M. Pham et al.
TESGNN: Temporal Equivariant Scene Graph Neural Networks for Efficient and Robust Multi-View 3D Scene Understanding
by Quang P. M. Pham, Khoi T. N. Nguyen, Lan C. Ngo, Truong Do, Dezhen Song, Truong-Son Hy
First submitted to arxiv on: 15 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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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 proposes a novel approach to scene graph generation, which plays a crucial role in various computer vision tasks. The authors highlight the importance of preserving symmetry when generating scene graphs from 3D point clouds, as current methods often overlook this critical aspect, leading to reduced accuracy and robustness. To address these challenges, they introduce Temporal Equivariant Scene Graph Neural Network (TESGNN), a combined architecture consisting of an Equivariant Scene Graph Neural Network (ESGNN) and a Temporal Graph Matching Network. TESGNN outperforms current state-of-the-art methods in scene graph generation, achieving higher accuracy and faster training convergence. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about creating a new way to understand scenes from 3D point clouds. It’s like taking a picture of a room, but instead of just seeing what’s there, it tries to understand how everything is related. Right now, most methods don’t do this very well because they ignore the fact that some things in the scene are symmetrical – like two chairs facing each other. The authors of this paper created a new method called TESGNN that solves this problem and does even better than current best methods. It’s also really good at handling noisy or changing data, which is important for real-world applications like robotics. |
Keywords
» Artificial intelligence » Graph neural network
