Neural 3D Mesh Renderer (1711.07566v1)

Abstract: For modeling the 3D world behind 2D images, which 3D representation is most appropriate? A polygon mesh is a promising candidate for its compactness and geometric properties. However, it is not straightforward to model a polygon mesh from 2D images using neural networks because the conversion from a mesh to an image, or rendering, involves a discrete operation called rasterization, which prevents back-propagation. Therefore, in this work, we propose an approximate gradient for rasterization that enables the integration of rendering into neural networks. Using this renderer, we perform single-image 3D mesh reconstruction with silhouette image supervision and our system outperforms the existing voxel-based approach. Additionally, we perform gradient-based 3D mesh editing operations, such as 2D-to-3D style transfer and 3D DeepDream, with 2D supervision for the first time. These applications demonstrate the potential of the integration of a mesh renderer into neural networks and the effectiveness of our proposed renderer.

• The paper introduces an approximate gradient for rasterization that enables back-propagation through the rendering pipeline.
• It demonstrates superior single-image 3D reconstruction performance over voxel-based methods using silhouette supervision.
• The framework supports novel gradient-based mesh editing applications, including 2D-to-3D style transfer and 3D DeepDream.

Integration of 3D Mesh Rendering with Neural Networks for Enhanced 3D Understanding and Editing

The paper "Neural 3D Mesh Renderer" by Hiroharu Kato, Yoshitaka Ushiku, and Tatsuya Harada addresses the challenge of integrating 3D mesh rendering into neural network frameworks, particularly convolutional neural networks (CNNs), for enhanced 3D understanding and editing tasks. The authors introduce an innovative approach to approximate the gradients of the rasterization process, thus enabling back-propagation through the rendering pipeline, which traditionally involved non-differentiable operations.

Summary of Contributions

1. Approximate Gradient for Rasterization: The authors propose a novel method to compute approximate gradients for rasterization, the process which converts vertex information to pixel information. This enables the embedding of the rendering process into neural networks, allowing end-to-end training.
2. Single-Image 3D Mesh Reconstruction: The proposed framework uses silhouette image supervision to perform reconstruction of 3D meshes from single 2D images. The results exhibit significant improvements over existing voxel-based approaches, reflecting both in visual fidelity and reconstruction accuracy.
3. Gradient-Based 3D Mesh Editing: By leveraging the differentiable renderer, the paper introduces novel 3D mesh editing tasks such as 2D-to-3D style transfer and 3D DeepDream, both of which employ gradient-based optimization methods on rendered images.
4. Code Release: The authors commit to releasing their Neural Renderer code, which serves as an open resource for further exploration and application in the research community.

Methodology

The core innovation lies in the proposed approximate gradient for the rasterization step. The authors circumvent the non-differentiability by introducing linear interpolations in place of discrete operations, thus enabling the flow of gradients through the rendering process. This methodology involves:

• Defining a gradient for mesh vertex positions by considering the effect of moving a vertex on the pixel intensity.
• Addressing scenarios with single and multiple faces, marketable through occlusion handling.
• Including textures and simple lighting models in the rendering process, thus broadening the applicability.

Applications and Results

Single-Image 3D Reconstruction: The paper's evaluation using the ShapeNetCore dataset demonstrates that the proposed mesh-based approach outperforms the traditional voxel-based methods. The reconstruction accuracy, measured via voxel Intersection over Union (IoU), shows better results in 10 out of 13 categories. The qualitative analysis highlights that the generated meshes exhibit higher resolution details and fewer artifacts compared to voxel-based representations. This is particularly evident in complex shapes like airplane wings and thin structures.

2D-to-3D Style Transfer and 3D DeepDream: The paper introduces creative applications of the renderer for mesh editing. The style transfer adapts the visual styles of 2D paintings to 3D meshes, resulting in visually coherent alterations in both texture and shape. The 3D DeepDream extends the popular 2D image modification technique to 3D objects, leading to the emergent, abstract features in the mesh structure.

Implications and Future Work

The integration of differentiable 3D mesh renderings into CNNs as demonstrated opens new avenues for more accurate and resource-efficient 3D understanding. The compactness and geometric properties of meshes make them an attractive alternative to voxels, particularly in applications requiring high-resolution representations and intricate surface details.

Future research could explore dynamic generation of faces-to-vertices relationships, overcoming the limitations in generating objects with diverse topologies. Additionally, applications could extend to interactive 3D modeling, augmented reality, and advanced computer graphics, where real-time, precise 3D reconstructions and edits are essential.

Conclusion

By addressing the non-differentiability of the rasterization process, the paper successfully integrates 3D mesh rendering with neural networks, paving the way for advancements in 3D reconstruction and editing. The applications presented not only demonstrate the efficacy and potential of the proposed approach but also indicate a broad scope for future developments in both theoretical and practical aspects of 3D computer vision and computational art.

In essence, the "Neural 3D Mesh Renderer" stands as a significant step towards the seamless integration of rendering processes into neural network architectures, marking a pivotal innovation in the domain of 3D understanding and manipulation.