Summary of Xmask3d: Cross-modal Mask Reasoning For Open Vocabulary 3d Semantic Segmentation, by Ziyi Wang et al.
XMask3D: Cross-modal Mask Reasoning for Open Vocabulary 3D Semantic Segmentation
by Ziyi Wang, Yanbo Wang, Xumin Yu, Jie Zhou, Jiwen Lu
First submitted to arxiv on: 20 Nov 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 paper proposes a novel approach, XMask3D, to improve open vocabulary 3D semantic segmentation. Existing methods focus on unified feature spaces, but struggle with fine-grained boundary delineation. XMask3D uses cross-modal mask reasoning to align 3D features and 2D-text embeddings at the mask level. A denoising UNet-based mask generator is developed, leveraging pre-trained diffusion models for precise textual control over pixel representations. 3D global features are integrated as implicit conditions into a pre-trained 2D denoising UNet, enabling geometry-aware segmentation masks. The generated 2D masks align with the vision-language feature space, augmenting open vocabulary capabilities. Finally, fused 2D and 3D mask features achieve competitive performance across multiple benchmarks for 3D open vocabulary semantic segmentation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper tries to improve a computer vision task called 3D semantic segmentation. Currently, methods focus on combining different types of data, but they don’t do it very well. The new approach, XMask3D, tries to match the features from different sources at a more detailed level. It uses a special type of neural network to generate masks that are more accurate and take into account 3D geometry. The results show that this approach performs better than others on several benchmarks. |
Keywords
» Artificial intelligence » Mask » Neural network » Semantic segmentation » Unet
