Question: This figure shows the pipeline from the paper 'MeshSegmenter: Zero-Shot Mesh Semantic Segmentation via Texture Synthesis'. What is the abbreviation for the middle step of this figure in the paper?
Date: 2024-07-18

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MeshSegmenter: Zero-Shot Mesh Semantic Segmentation via Texture Synthesis
We present MeshSegmenter, a simple yet effective framework designed for zero-shot 3D semantic segmentation. This model successfully extends the powerful capabilities of 2D segmentation models to 3D meshes, delivering accurate 3D segmentation across diverse meshes and segment descriptions. Specifically, our model leverages the Segment Anything Model (SAM) model to segment the target regions ...

MeshSegmenter: Zero-Shot Mesh Semantic Segmentation via Texture Synthesis
In this work, we have proposed MeshSegmenter, a pioneering framework for 3D zero-shot semantic segmentation. Our model extends the capabilities of 2D detection and segmentation models to accurately segment diverse 3D meshes by text. We propose generating textures based on object descriptions to enhance the accuracy of 2D segmentation models.

The implementation of MeshSegmenter: Zero-Shot Mesh Semantic ...
The implementation of MeshSegmenter: Zero-Shot Mesh Semantic Segmentation via Texture Synthesis. The code will be updated soon.

‪Jiale Xu‬ - ‪Google Scholar‬
Layout-Guided Novel View Synthesis from a Single Indoor Panorama ... Zero-Shot Mesh Semantic Segmentation via Texture Synthesis. Z Zhong, Y Xu, J Li, J Xu, Z Li, C Yu, S Gao ... Y Xiao, J Xu, S Gao. arXiv preprint arXiv:2201.06845, 2022. 2022: Supplementary Material for Dream3D: Zero-Shot Text-to-3D Synthesis Using 3D Shape Prior and Text-to ...

MeshSegmenter: Zero-Shot Mesh Semantic Segmentation via Texture ...
We present MeshSegmenter, a simple yet effective framework designed for zero-shot 3D semantic segmentation. This model successfully extends the powerful capabilities of 2D segmentation models to 3D meshes, delivering accurate 3D segmentation across diverse meshes and segment descriptions. Specifically, our model leverages the Segment Anything Model (SAM) model to segment the target regions ...

Chaohui Yu - Homepage
VCD-Texture: Variance Alignment based 3D-2D Co-Denoising for Text-Guided Texturing ... (ECCV-24) MeshSegmenter: Zero-Shot Mesh Semantic Segmentation via Texture Synthesis. Ziming Zhong, Yanxu Xu, Jing Li, Jiale Xu, Zhengxin Li, Chaohui Yu ... Foundation Model Drives Weakly Incremental Learning for Semantic Segmentation. Chaohui Yu, Qiang Zhou ...

Texture Synthesis | Papers With Code
The fundamental goal of example-based **Texture Synthesis** is to generate a texture, usually larger than the input, that faithfully captures all the visual characteristics of the exemplar, yet is neither identical to it, nor exhibits obvious unnatural looking artifacts. ... MeshSegmenter: Zero-Shot Mesh Semantic Segmentation via Texture ...

MeshSegmenter: Zero-Shot Mesh Semantic Segmentation via Texture Synthesis
MeshSegmenter: Zero-Shot Mesh Semantic Segmentation via Texture Synthesis We present MeshSegmenter, a simple yet effective framework designed for zero-shot 3D semantic segmentation. This model successfully extends the powerful capabilities of 2D segmentation models to 3D meshes, delivering accurate 3D segmentation across diverse meshes and ...

Answer: FCR