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

Radiance field reconstruction aims to recover high-quality 3D representations from multi-view RGB images. Recent advances, such as 3D Gaussian splatting, have achieved real-time rendering with high visual fidelity, given sufficiently powerful graphics hardware. However, drastic model simplification — i.e., reducing the number of primitives by several orders of magnitude — is required to enable efficient online transmission and rendering across diverse hardware platforms. We introduce DiffSoup, a radiance field representation that employs a soup (i.e., a highly unstructured primitives) of a small number of triangles with neural textures that have binary opacity. We show that the binary opacity representation is directly differentiable via stochastic opacity masking, enabling stable training without molifier (i.e., smooth rasterization). DiffSoup can be rasterized with a traditional depth-testing framework, allowing the optimized scenes to be seamlessly integrated into conventional graphics pipelines and rendered interactively on consumer-grade laptops and mobile devices.