Summary of Bootstraping Clustering Of Gaussians For View-consistent 3d Scene Understanding, by Wenbo Zhang et al.
Bootstraping Clustering of Gaussians for View-consistent 3D Scene Understanding
by Wenbo Zhang, Lu Zhang, Ping Hu, Liqian Ma, Yunzhi Zhuge, Huchuan Lu
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 The paper proposes FreeGS, an unsupervised framework for injecting semantics into 3D Gaussian Splatting (3DGS). Unlike existing approaches that rely on 2D supervision, FreeGS achieves view-consistent 3D scene understanding without 2D labels. The framework introduces the IDentity-coupled Semantic Field (IDSF) to capture semantic representations and view-consistent instance indices for each Gaussian. Two-step alternating optimization is used to optimize IDSF, with semantics helping to extract coherent instances in 3D space and resulting instances regularizing the injection of stable semantics from 2D space. A 2D-3D joint contrastive loss enhances the complementarity between view-consistent 3D geometry and rich semantics during bootstrapping. FreeGS is evaluated on LERF-Mask, 3D-OVS, and ScanNet datasets for tasks like novel-view semantic segmentation, object selection, and 3D object detection, showing comparable performance to state-of-the-art methods while avoiding complex data preprocessing. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new way to understand scenes in 3D space without needing labels from the 2D world. They call this method FreeGS. Instead of using 2D labels, FreeGS uses information about objects and their relationships in 3D space. This allows it to understand scenes consistently across different views without needing lots of data preparation. The authors tested FreeGS on several datasets and found that it performs as well as other methods while avoiding the need for complex data preprocessing. |
Keywords
» Artificial intelligence » Bootstrapping » Contrastive loss » Mask » Object detection » Optimization » Scene understanding » Semantic segmentation » Semantics » Unsupervised
