Summary of T-3dgs: Removing Transient Objects For 3d Scene Reconstruction, by Alexander Markin et al.
T-3DGS: Removing Transient Objects for 3D Scene Reconstruction
by Alexander Markin, Vadim Pryadilshchikov, Artem Komarichev, Ruslan Rakhimov, Peter Wonka, Evgeny Burnaev
First submitted to arxiv on: 29 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 A novel framework called T-3DGS is proposed to robustly filter out transient objects during 3D scene reconstruction. This framework consists of two steps: an unsupervised classification network that distinguishes transient objects from static scene elements, and a refinement step that integrates off-the-shelf segmentation and bidirectional tracking modules. The proposed approach is evaluated on sparsely and densely captured video datasets, outperforming state-of-the-art methods in terms of high-fidelity 3D reconstructions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of researchers developed a new way to create 3D models from videos. They wanted to get rid of moving objects that can ruin the quality of the final product. To do this, they created a two-part system: first, they taught a computer to recognize which parts of the video are moving and which are not; then, they used another technique to make sure the boundaries between these objects are accurate and consistent over time. The results show that their method works better than other approaches in real-world scenarios. |
Keywords
» Artificial intelligence » Classification » Tracking » Unsupervised
