Summary of Tsgaussian: Semantic and Depth-guided Target-specific Gaussian Splatting From Sparse Views, by Liang Zhao et al.
TSGaussian: Semantic and Depth-Guided Target-Specific Gaussian Splatting from Sparse Views
by Liang Zhao, Zehan Bao, Yi Xie, Hong Chen, Yaohui Chen, Weifu Li
First submitted to arxiv on: 13 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 proposed TSGaussian framework combines semantic constraints with depth priors to improve novel view synthesis tasks. By prioritizing computational resources on designated targets, it minimizes background allocation and achieves superior results compared to state-of-the-art methods. The approach leverages bounding boxes from YOLOv9 as prompts for the Segment Anything Model to generate 2D mask predictions, ensuring semantic accuracy and cost efficiency. Additionally, TSGaussian clusters 3D gaussians using a compact identity encoding and incorporates 3D spatial consistency regularization. This leads to a pruning strategy that effectively reduces redundancy in 3D gaussians. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces a new framework called TSGaussian, which helps reconstruct specific objects with complex structures from sparse views. It uses a combination of semantic constraints and depth priors to improve novel view synthesis tasks. The approach is efficient and effective, achieving better results than other methods on standard datasets and a new challenging dataset. |
Keywords
» Artificial intelligence » Mask » Pruning » Regularization
