Mesh2NeRF: Direct Mesh Supervision for Neural Radiance Field Representation and Generation (2403.19319v2)

Abstract: We present Mesh2NeRF, an approach to derive ground-truth radiance fields from textured meshes for 3D generation tasks. Many 3D generative approaches represent 3D scenes as radiance fields for training. Their ground-truth radiance fields are usually fitted from multi-view renderings from a large-scale synthetic 3D dataset, which often results in artifacts due to occlusions or under-fitting issues. In Mesh2NeRF, we propose an analytic solution to directly obtain ground-truth radiance fields from 3D meshes, characterizing the density field with an occupancy function featuring a defined surface thickness, and determining view-dependent color through a reflection function considering both the mesh and environment lighting. Mesh2NeRF extracts accurate radiance fields which provides direct supervision for training generative NeRFs and single scene representation. We validate the effectiveness of Mesh2NeRF across various tasks, achieving a noteworthy 3.12dB improvement in PSNR for view synthesis in single scene representation on the ABO dataset, a 0.69 PSNR enhancement in the single-view conditional generation of ShapeNet Cars, and notably improved mesh extraction from NeRF in the unconditional generation of Objaverse Mugs.

• The paper introduces Mesh2NeRF, a method that uses direct mesh supervision to compute neural radiance fields and enhance 3D content generation.
• It presents an analytical occupancy model that estimates density and view-dependent colors, bypassing traditional view synthesis and reducing artifacts.
• The approach demonstrates significant PSNR improvements in both single-scene and conditional 3D generation tasks across multiple datasets.

A Technical Overview of Mesh2NeRF: Direct Mesh Supervision for Neural Radiance Field Representation and Generation

The paper "Mesh2NeRF: Direct Mesh Supervision for Neural Radiance Field Representation and Generation" presents a method that innovates in the domain of 3D content generation and neural representation learning. In the context of Neural Radiance Fields (NeRF), this work proposes Mesh2NeRF, a novel approach to derive ground-truth radiance fields from textured 3D meshes. This approach represents a strategic departure from traditional methods that rely heavily on multi-view rendering to fit synthetic datasets, which are often prone to occlusions and under-fitting issues.

Method Overview

Mesh2NeRF introduces an analytical approach to directly compute radiance fields using mesh data as supervision. This is achieved by characterizing density fields through an occupancy function that defines surface thickness and models view-dependent color under lighting influences. The process bypasses the intermediate rendering stage, thereby minimizing inconsistencies and artifacts commonly associated with view synthesis based on 2D data.

The core contribution of Mesh2NeRF lies in its ability to serve as direct 3D supervision for NeRFs. By using the occupancy and reflection functions to encapsulate the data from meshes, this method provides precise density and color estimations for sampled points along rays, enabling efficient volumetric rendering. The authors incorporate this supervision into existing generative models, substantially improving performance in single- and multi-scene scenarios.

Numerical Results and Implications

The authors validate Mesh2NeRF's efficacy across several datasets, showcasing impressive numerical gains. For instance, in single-scene representation tasks on the ABO dataset, Mesh2NeRF offers a 3.12 dB improvement in PSNR compared to baseline methods. Furthermore, it demonstrates a 0.69 PSNR enhancement on the single-view conditional generation of ShapeNet Cars. These results underscore the method's robustness in synthesizing accurate geometric and textural representations, effectively advancing several 3D generation tasks, including conditional and unconditional NeRF generation.

Theoretical and Practical Implications

Theoretically, Mesh2NeRF suggests that leveraging hybrid representations, such as combining meshes with radiance fields, can address the ambiguities and limitations of solely using neural implicit representations. This work pushes the boundary towards understanding how existing high-quality mesh datasets can be utilized as reliable training data for neural representations without the extensive requirement for rendered data collection.

Practically, the paper has implications for industries reliant on 3D content creation. By enhancing the quality and accuracy of 3D asset generation, this approach could streamline content generation pipelines, reduce production costs, and improve the fidelity of real-time applications, such as VR/AR environments and gaming.

Future Directions

Future work could explore extending Mesh2NeRF with more sophisticated lighting models beyond the current BRDF approach to enhance realism in varying illumination contexts. Additionally, investigating the integration of Mesh2NeRF with real-time systems could demonstrate its applicability in dynamic content creation. The prospect of refining and adapting this method for unlabeled or less structured mesh data also presents a valuable trajectory for broader application.

In summary, Mesh2NeRF presents a compelling case for direct mesh supervision in NeRFs, substantially enhancing the landscape of 3D generation methodologies. By addressing the inherent challenges of traditional radiance field training, this work lays the foundation for more advanced, efficient, and realistic 3D scene reconstruction and generation.