MeshAnything mimics human artists in extracting meshes from any 3D representations. It can be combined with various 3D asset production pipelines, such as 3D reconstruction and generation, to convert their results into Artist-Created Meshes that can be seamlessly applied in the 3D industry.
Description: Efficient generation of 3D assets can revolutionize multiple industries.
MeshAnything generates meshes with hundreds of times fewer faces, significantly improving storage, rendering, and simulation efficiencies, while achieving precision comparable to previous methods.
Abstract
Recently, 3D assets created via reconstruction and generation have matched the quality of manually crafted assets, highlighting their potential for replacement. However, this potential remains largely unrealized because these assets always need to be converted to meshes for 3D industry applications. Unfortunately, the meshes produced by current mesh extraction methods are significantly inferior to Artist-Created Meshes (AMs), i.e., meshes created by human artists.
Specifically, current mesh extraction methods rely on dense faces and ignore geometric features, leading to inefficiencies, complicated post-processing, and lower representation quality. To address these issues, MeshAnything introduces a novel approach by treating mesh extraction as a generation problem, producing AMs aligned with specified shapes. By converting 3D assets in any 3D representation into AMs, MeshAnything can be integrated with various 3D asset production methods, thereby enhancing their application across the 3D industry.
Methodology
The architecture of MeshAnything comprises a VQ-VAE and a shape-conditioned decoder-only transformer. Initially, it learns a mesh vocabulary using the VQ-VAE. We then train the shape-conditioned decoder-only transformer on this vocabulary for shape-conditioned autoregressive mesh generation.
Extensive experiments have shown that our method generates AMs with significantly fewer faces, enhancing storage, rendering, and simulation efficiencies while achieving a precision similar to previous methods. MeshAnything samples point clouds from given 3D assets, encodes them into features, and injects them into the decoder-only transformer to achieve shape-conditional mesh generation. Compared to methods like MeshGPT, which directly generate Artist-Created Meshes, our approach avoids learning complex 3D shape distributions. Instead, it focuses on efficiently constructing shapes through optimized topology, significantly reducing the training burden and enhancing scalability.
Results
By integrating MeshAnything with various 3D asset production methods, our approach achieves highly controllable Artist-Created Mesh generation. We compare our results with ground truth: In one instance, MeshAnything generates meshes with better topology and fewer faces than the ground truth. In another, we produce meshes with a completely different topology while achieving a similar shape, proving that our method does not simply overfit but understands how to construct meshes using efficient topology.
Remember these 3 key ideas for your startup:
Efficiency in Storage and Rendering: MeshAnything generates meshes with hundreds of times fewer faces, significantly improving storage, rendering, and simulation efficiencies. This means your startup can handle 3D data more efficiently and at a lower cost.
Human-like Extraction: MeshAnything closely mimics a human artist extracting meshes, resulting in high-quality outputs that are suitable for the 3D industry. This quality can streamline the integration of your 3D assets into production pipelines.
Scalability and Precision: The methodology behind MeshAnything makes it scalable and capable of high precision, without the need for complex distributions. This provides a robust framework for startups dealing with large datasets and requiring adaptable solutions. For advice on how entrepreneurs can benefit and learn from project managers, consider integrating such insights into your organizational strategy.
Edworking is the best and smartest decision for SMEs and startups to be more productive. Edworking is a FREE superapp of productivity that includes all you need for work powered by AI in the same superapp, connecting Task Management, Docs, Chat, Videocall, and File Management. Save money today by not paying for Slack, Trello, Dropbox, Zoom, and Notion.
For more details, see the original source.
