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Radiant Foam: Real-Time Differentiable Ray Tracing
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Research on differentiable scene representations is consistently moving towards more efficient, real-time models. Recently, this has led to the popularization of splatting methods, which eschew the traditional ray-based rendering of radiance fields in favor of rasterization. This has yielded a significant improvement in rendering speeds due to the efficiency of rasterization algorithms and hardware, but has come at a cost: the approximations that make rasterization efficient also make implementation of light transport phenomena like reflection and refraction much more difficult. We propose a novel scene representation which avoids these approximations, but keeps the efficiency and reconstruction quality of splatting by leveraging a decades-old efficient volumetric mesh ray tracing algorithm which has been largely overlooked in recent computer vision research. The resulting model, which we name Radiant Foam, achieves rendering speed and quality comparable to Gaussian Splatting, without the constraints of rasterization. Unlike ray traced Gaussian models that use hardware ray tracing acceleration, our method requires no special hardware or APIs beyond the standard features of a programmable GPU.
Forward citations
Cited by 3 Pith papers
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ContraGS: Codebook-Condensed and Trainable Gaussian Splatting for Fast, Memory-Efficient Reconstruction
ContraGS trains 3D Gaussian Splatting directly on codebook-compressed representations, cutting peak model memory ~3.5x with small quality loss.
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Spherical Voronoi: Directional Appearance as a Differentiable Partition of the Sphere
Spherical Voronoi—a softmax partition of the sphere with learnable sites—is proposed as a differentiable appearance basis for Gaussian splatting, improving view-dependent radiance and reflection modeling.
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A Bag of Tricks for Efficient Implicit Neural Point Clouds
A curated set of sampling, rasterization, and CNN pretraining tricks doubles rendering speed and cuts training time and memory of implicit neural point clouds with no loss of image quality.
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