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.

翻译：辐射场重建旨在从多视角RGB图像中恢复高质量的三维表示。近期进展（如三维高斯泼溅）能够在性能充足的图形硬件上实现高视觉保真度的实时渲染。然而，跨平台的高效在线传输与渲染要求对模型进行大幅简化，将图元数量减少数个量级。我们提出DiffSoup——一种采用少量三角形汤（即高度非结构化集合）的辐射场表示方法，该表示结合了神经纹理与二元不透明度。我们证明，通过随机不透明度掩码技术，这种二元不透明度表示可直接进行微分，无需使用平滑函数（即光滑光栅化）即可实现稳定训练。DiffSoup支持标准深度测试光栅化，可无缝集成至传统图形管线，并在消费级笔记本与移动设备上实现交互式渲染。代码开源于https://github.com/kenji-tojo/diffsoup。