Reproduce “Neural 3D Mesh Renderer” on OpenResearch
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About this paper
Hiroharu Kato, Yoshitaka Ushiku, Tatsuya Harada
The authors introduce a mesh renderer that keeps rasterization in the forward pass and supplies geometry-based approximate gradients in the backward pass. They demonstrate silhouette-supervised single-image reconstruction, style transfer, and DeepDream. Reconstruction reaches voxel intersection-over-union of 0.6016, versus 0.5736 for a voxel baseline, but uses fixed-topology meshes and scores higher in 10 of 13 categories.
Key insights
- Replacing discrete pixel-triangle relationships with smooth, continuous approximations enables gradient flow through the rendering process.
- By carefully approximating gradients and applying conditional flow, 2D image-based losses can effectively optimize 3D mesh geometry and appearance.
- Deforming a predefined base mesh simplifies the 3D reconstruction problem, allowing focus on shape refinement rather than complex topology generation.
Summary by alphaXiv.
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