AI BriefingGaussian Point Splatting
Key point
We propose a stochastic Gaussian splatting technique that can render hundreds of millions of Gaussians in real time.
Details
We propose a stochastic approach that samples pixel-sized opaque points from Gaussians and splats them onto the framebuffer using 64-bit atomics.
By leveraging parallel programming primitives, the workload is evenly distributed across millions of threads, and computation is further accelerated through hierarchical frustum and occlusion culling.
This method achieves outstanding scalability, enabling real-time rendering of hundreds of millions of Gaussians. Compared to existing Gaussian Splatting, slight differences in noise or aliasing may occur, but rendering fidelity is maintained.
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