DiffSoup: Direct Differentiable Rasterization of Triangle Soup for Extreme Radiance Field Simplification

Abstract

Radiance field reconstruction aims to recover high-quality 3D representations from multi-view RGB images. Recent advances, such as 3D Gaussian splatting, enable real-time rendering with high visual fidelity on sufficiently powerful graphics hardware. However, efficient online transmission and rendering across diverse platforms requires drastic model simplification, reducing the number of primitives by several orders of magnitude. We introduce DiffSoup, a radiance field representation that employs a soup (i.e., a highly unstructured set) of a small number of triangles with neural textures and binary opacity. We show that this binary opacity representation is directly differentiable via stochastic opacity masking, enabling stable training without a mollifier (i.e., smooth rasterization). DiffSoup can be rasterized using standard depth testing, enabling seamless integration into traditional graphics pipelines and interactive rendering on consumer-grade laptops and mobile devices. Code is available at https://github.com/kenji-tojo/diffsoup.

Figures (12)

• Figure 1: (a) Given multi-view RGB images, (b) we reconstruct a simplified radiance field as a textured triangle soup using differentiable rasterization. (c) The resulting scene can be rendered within a standard depth-tested rasterization pipeline, enabling seamless visualization across devices—including a high-end desktop with a dedicated GPU, a consumer-grade laptop (MacBook Pro with an M3 Pro chip), and even a smartphone (iPhone 15).
• Figure 2: Overview of our algorithm. (a) We represent radiance fields as a textured triangle soup, where barycentric coordinates map to learnable color and alpha textures. (b) At run time, this representation is rendered with binary opacity, enabling efficient rasterization via standard depth testing. (c) Rasterization produces a high-dimensional color-feature image, which is then mapped to RGB values using a small shared MLP trained to minimize the error between the rendering and the ground-truth image. (d) During training, we initialize triangles around structure-from-motion (SfM) keypoints. (e) We optimize opacity through stochastic opacity masking, while vertex positions are updated using edge gradients for opaque triangles.
• Figure 3: (a) Alpha blending computes cumulative transmittance over depth-sorted samples and produces the final color as a weighted average. (b) Our stochastic opacity masking, in contrast, simulates a discrete stochastic process over the surface, allowing the renderer to output the exact color of the selected fragment.
• Figure 4: Multi-resolution triangle texture. Top: We parameterize the texture using multiple grid resolutions, corresponding to different triangle subdivision levels. Bottom: (a) At run time, the renderer queries only the highest-resolution triangle texture. (b) At training time, this highest-resolution texture is over-parameterized with lower-resolution grids to improve optimization stability.
• Figure 5: Qualitative results on the MipNeRF360barron2022mipnerf360 dataset. (Left to right) Ground-truth test images; views synthesized by 3DGS kerbl3Dgaussians, Triangle Splatting Held2025Triangle, TexGS chao2025texturedgaussians, our method; and a visualization of our reconstructed triangle geometry. Rows correspond to different scenes, and all models use 15K primitives.