NeuMesh++: Towards Versatile and Efficient Volumetric Editing with Disentangled Neural Mesh-based Implicit Field

2026-06-17 · Source: Computer Vision and Pattern Recognition · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Computer Vision · Depth: Expert, quick

Summary

NeuMesh++ introduces a novel mesh-based representation designed for versatile and efficient volumetric editing, addressing limitations in existing neural rendering methods that offer restricted functionalities like rigid transformation or category-specific editing. This new approach encodes neural radiance fields by disentangling geometry, texture, and semantic codes directly onto mesh vertices. This architecture facilitates a comprehensive suite of editing capabilities, including mesh-guided geometry adjustments, designated texture operations such as swapping, filling, and painting, and semantic-guided modifications. To achieve this, NeuMesh++ incorporates several key techniques: a local space parameterization for enhanced rendering quality and training stability, a learnable modification color on vertices for improved texture editing fidelity, a spatial-aware optimization strategy for precise texture manipulation, and semantic-aided region selection to simplify the annotation process for implicit field editing. Extensive experiments on both real and synthetic datasets confirm its superior representation quality and editing prowess.

Key takeaway

For Computer Vision Engineers developing 3D content creation tools, NeuMesh++ offers a robust solution for overcoming current neural rendering limitations. You can achieve highly versatile volumetric editing by leveraging its disentangled mesh-based representation for geometry, texture, and semantic control. This approach simplifies complex tasks like precise texture painting or semantic-guided object manipulation, significantly streamlining your workflow for both real and synthetic datasets. Consider integrating these techniques to enhance the fidelity and efficiency of your 3D scene editing applications.

Key insights

NeuMesh++ enables versatile 3D volumetric editing by disentangling geometry, texture, and semantics on mesh vertices for comprehensive control.

Principles

• Disentangling scene properties on mesh vertices enhances editing versatility.
• Local space parameterization improves neural rendering stability and quality.
• Semantic guidance simplifies complex implicit field region selection.

Method

NeuMesh++ encodes neural radiance fields with disentangled geometry, texture, and semantic codes on mesh vertices. It uses local space parameterization, learnable vertex modification colors, spatial-aware optimization, and semantic-aided region selection for comprehensive editing.

In practice

• Apply mesh-guided geometry editing for precise shape adjustments.
• Perform texture swapping, filling, or painting on 3D models.
• Utilize semantic-aided selection to simplify complex region annotations.

Topics

• NeuMesh++
• Neural Radiance Fields
• Volumetric Editing
• Mesh-based Representation
• 3D Scene Editing
• Computer Vision

Best for: Research Scientist, AI Scientist, Computer Vision Engineer, Machine Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Computer Vision and Pattern Recognition.