MeshLRM: Large Reconstruction Model for High-Quality Mesh

• URL: http://arxiv.org/abs/2404.12385v1
• Date: Thu, 18 Apr 2024 17:59:41 GMT
• Title: MeshLRM: Large Reconstruction Model for High-Quality Mesh
• Authors: Xinyue Wei, Kai Zhang, Sai Bi, Hao Tan, Fujun Luan, Valentin Deschaintre, Kalyan Sunkavalli, Hao Su, Zexiang Xu,
• Abstract summary: MeshLRM can reconstruct a high-quality mesh from merely four input images in less than one second. Our approach achieves state-of-the-art mesh reconstruction from sparse-view inputs and also allows for many downstream applications.
• Score: 52.71164862539288
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose MeshLRM, a novel LRM-based approach that can reconstruct a high-quality mesh from merely four input images in less than one second. Different from previous large reconstruction models (LRMs) that focus on NeRF-based reconstruction, MeshLRM incorporates differentiable mesh extraction and rendering within the LRM framework. This allows for end-to-end mesh reconstruction by fine-tuning a pre-trained NeRF LRM with mesh rendering. Moreover, we improve the LRM architecture by simplifying several complex designs in previous LRMs. MeshLRM's NeRF initialization is sequentially trained with low- and high-resolution images; this new LRM training strategy enables significantly faster convergence and thereby leads to better quality with less compute. Our approach achieves state-of-the-art mesh reconstruction from sparse-view inputs and also allows for many downstream applications, including text-to-3D and single-image-to-3D generation. Project page: https://sarahweiii.github.io/meshlrm/

Related papers

• RelitLRM: Generative Relightable Radiance for Large Reconstruction Models [52.672706620003765]
  We propose RelitLRM for generating high-quality Gaussian splatting representations of 3D objects under novel illuminations. Unlike prior inverse rendering methods requiring dense captures and slow optimization, RelitLRM adopts a feed-forward transformer-based model. We show our sparse-view feed-forward RelitLRM offers competitive relighting results to state-of-the-art dense-view optimization-based baselines.
  arXiv  Detail & Related papers  (2024-10-08T17:40:01Z)
• $R^2$-Mesh: Reinforcement Learning Powered Mesh Reconstruction via Geometry and Appearance Refinement [5.810659946867557]
  Mesh reconstruction based on Neural Radiance Fields (NeRF) is popular in a variety of applications such as computer graphics, virtual reality, and medical imaging. We propose a novel algorithm that progressively generates and optimize meshes from multi-view images. Our method delivers highly competitive and robust performance in both mesh rendering quality and geometric quality.
  arXiv  Detail & Related papers  (2024-08-19T16:33:17Z)
• M-LRM: Multi-view Large Reconstruction Model [37.46572626325514]
  Multi-view Large Reconstruction Model (M-LRM) designed to efficiently reconstruct high-quality 3D shapes from multi-views in a 3D-aware manner. Compared to Large Reconstruction Model, the proposed M-LRM can produce a tri-plane NeRF with $128 times 128$ resolution and generate 3D shapes of high fidelity.
  arXiv  Detail & Related papers  (2024-06-11T18:29:13Z)
• GTR: Improving Large 3D Reconstruction Models through Geometry and Texture Refinement [51.97726804507328]
  We propose a novel approach for 3D mesh reconstruction from multi-view images. Our method takes inspiration from large reconstruction models that use a transformer-based triplane generator and a Neural Radiance Field (NeRF) model trained on multi-view images.
  arXiv  Detail & Related papers  (2024-06-09T05:19:24Z)
• InstantMesh: Efficient 3D Mesh Generation from a Single Image with Sparse-view Large Reconstruction Models [66.83681825842135]
  InstantMesh is a feed-forward framework for instant 3D mesh generation from a single image. It features state-of-the-art generation quality and significant training scalability. We release all the code, weights, and demo of InstantMesh with the intention that it can make substantial contributions to the community of 3D generative AI.
  arXiv  Detail & Related papers  (2024-04-10T17:48:37Z)
• CRM: Single Image to 3D Textured Mesh with Convolutional Reconstruction Model [37.75256020559125]
  We present a high-fidelity feed-forward single image-to-3D generative model. We highlight the necessity of integrating geometric priors into network design. Our model delivers a high-fidelity textured mesh from an image in just 10 seconds, without any test-time optimization.
  arXiv  Detail & Related papers  (2024-03-08T04:25:29Z)
• LRM: Large Reconstruction Model for Single Image to 3D [61.47357798633123]
  We propose the first Large Reconstruction Model (LRM) that predicts the 3D model of an object from a single input image within just 5 seconds. LRM adopts a highly scalable transformer-based architecture with 500 million learnable parameters to directly predict a neural radiance field (NeRF) from the input image. We train our model in an end-to-end manner on massive multi-view data containing around 1 million objects.
  arXiv  Detail & Related papers  (2023-11-08T00:03:52Z)
• PixMIM: Rethinking Pixel Reconstruction in Masked Image Modeling [83.67628239775878]
  Masked Image Modeling (MIM) has achieved promising progress with the advent of Masked Autoencoders (MAE) and BEiT. This paper undertakes a fundamental analysis of MIM from the perspective of pixel reconstruction. We propose a remarkably simple and effective method, ourmethod, that entails two strategies.
  arXiv  Detail & Related papers  (2023-03-04T13:38:51Z)
• Residual Multiplicative Filter Networks for Multiscale Reconstruction [24.962697695403037]
  We introduce a new coordinate network architecture and training scheme that enables coarse-to-fine optimization with fine-grained control over the frequency support of learned reconstructions. We demonstrate how these modifications enable multiscale optimization for coarse-to-fine fitting to natural images. We then evaluate our model on synthetically generated datasets for the the problem of single-particle cryo-EM reconstruction.
  arXiv  Detail & Related papers  (2022-06-01T20:16:28Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.