Summary of Crm: Single Image to 3d Textured Mesh with Convolutional Reconstruction Model, by Zhengyi Wang et al.
CRM: Single Image to 3D Textured Mesh with Convolutional Reconstruction Model
by Zhengyi Wang, Yikai Wang, Yifei Chen, Chendong Xiang, Shuo Chen, Dajiang Yu, Chongxuan Li, Hang Su, Jun Zhu
First submitted to arxiv on: 8 Mar 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 presents the Convolutional Reconstruction Model (CRM), a feed-forward generative model that leverages geometric priors to generate high-quality 3D models from single images. The authors highlight the limitations of transformer-based methods in incorporating spatial information, which can lead to sub-optimal results when working with limited 3D data and slow training times. CRM uses a convolutional U-Net to align pixel-level details across orthographic views, allowing for efficient generation of textured meshes. By employing Flexicubes as a geometric representation, the model achieves direct end-to-end optimization on textured meshes. The authors demonstrate CRM’s capabilities by generating high-fidelity textured meshes from images in just 10 seconds without any test-time optimization. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you take a picture of something, and then you want to create a detailed 3D model of that thing. This is a difficult task because we don’t have much data on how things look from different angles. The authors of this paper have developed a new way to do this using a special kind of computer program called the Convolutional Reconstruction Model (CRM). This program uses information about how things look from multiple angles to create a detailed 3D model. It’s like taking six pictures of an object from different sides and then combining them into one complete picture. The authors show that their program can generate high-quality 3D models quickly, without needing any special training or additional data. |
Keywords
* Artificial intelligence * Generative model * Optimization * Transformer
