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Volumetrically Consistent 3D Gaussian Rasterization

About

Recently, 3D Gaussian Splatting (3DGS) has enabled photorealistic view synthesis at high inference speeds. However, its splatting-based rendering model makes several approximations to the rendering equation, reducing physical accuracy. We show that the core approximations in splatting are unnecessary, even within a rasterizer; We instead volumetrically integrate 3D Gaussians directly to compute the transmittance across them analytically. We use this analytic transmittance to derive more physically-accurate alpha values than 3DGS, which can directly be used within their framework. The result is a method that more closely follows the volume rendering equation (similar to ray-tracing) while enjoying the speed benefits of rasterization. Our method represents opaque surfaces with higher accuracy and fewer points than 3DGS. This enables it to outperform 3DGS for view synthesis (measured in SSIM and LPIPS). Being volumetrically consistent also enables our method to work out of the box for tomography. We match the state-of-the-art 3DGS-based tomography method with fewer points. Our code is publicly available at: https://github.com/chinmay0301ucsd/Vol3DGS

Chinmay Talegaonkar, Yash Belhe, Ravi Ramamoorthi, Nicholas Antipa• 2024

Related benchmarks

TaskDatasetResultRank
Novel View SynthesisTanks&Temples (test)
PSNR23.67
239
Novel View SynthesisMip-NeRF 360 (test)
PSNR27.3
166
Novel View SynthesisDeep Blending (test)
PSNR29.72
64
Novel View SynthesisMip-NeRF360 (test)
PSNR27.44
58
Novel View SynthesisDeepBlending (test)
PSNR29.61
43
Novel View SynthesisTank & Temples (test)
PSNR23.74
23
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