In plain words: Builds 3D scenes from simple solid shapes—octahedra and tetrahedra—instead of the usual tiny glowing blobs, using a fast GPU tool that learns them from photos. It rebuilt real scenes about as well as today's best methods while needing fewer shapes.
Abstract
Volumetric rendering has become central to modern novel view synthesis methods, which use differentiable rendering to optimize 3D scene representations directly from observed views. While many recent works build on NeRF or 3D Gaussians, we explore an alternative volumetric scene representation. More specifically, we introduce two new scene representations based on linear primitives - octahedra and tetrahedra - both of which define homogeneous volumes bounded by triangular faces. To optimize these primitives, we present a differentiable rasterizer that runs efficiently on GPUs, allowing end-to-end gradient-based optimization while maintaining real-time rendering capabilities. Through experiments on real-world datasets, we demonstrate comparable performance to state-of-the-art volumetric methods while requiring fewer primitives to achieve similar reconstruction fidelity. Our findings deepen the understanding of 3D representations by providing insights into the fidelity and performance characteristics of transparent polyhedra and suggest that adopting novel primitives can expand the available design space.
Nicolas von Lützow, Matthias Nießner
arXiv:2501.16312 · cs.CV · submitted Jan 27, 2025 · updated Oct 16, 2025
abstract · pdf · html · Project page: https://nicolasvonluetzow.github.io/LinPrim - Project video: https://youtu.be/NRRlmFZj5KQ - Accepted at NeurIPS 2025