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Researchers propose Gaussian Point Splatting, a stochastic rendering method using pixel-sized opaque points and 64-bit GPU atomics that scales to hundreds of millions of Gaussians in real time. The method, accepted at SIGGRAPH 2026, employs hierarchical culling and parallel programming primitives to achieve even workload distribution with only minor noise differences compared to original Gaussian splatting.
ZipSplat is a token-based feed-forward 3D Gaussian Splatting model that uses k-means clustering to decouple Gaussian placement from the pixel grid, achieving ~6x fewer Gaussians while setting new state-of-the-art results on DL3DV and RealEstate10K without requiring ground-truth poses or intrinsics.
RayDer is a unified feed-forward transformer that consolidates camera estimation, scene reconstruction, and rendering for self-supervised novel view synthesis from real-world video, achieving clean power-law scaling and strong zero-shot performance.
RT-Splatting introduces a new 3D Gaussian Splatting framework that separates geometric occupancy from optical opacity to improve rendering of semi-transparent specular surfaces with high-fidelity reflections and transmission.
MoCam is a research paper introducing a diffusion-based framework for unified novel view synthesis that dynamically coordinates geometric and appearance priors to improve robustness against geometric errors.
SplatWeaver is a feed-forward novel view synthesis framework that dynamically allocates 3D Gaussian primitives based on spatial complexity, improving rendering quality and efficiency over fixed-allocation methods. It leverages cardinality Gaussian experts and a pixel-level routing scheme guided by high-frequency priors to adaptively distribute primitives across complex and smooth scene regions.
GlobalSplat introduces an efficient feed-forward framework for 3D Gaussian splatting that achieves compact and consistent scene reconstruction using global scene tokens, reducing computational overhead and inference time to under 78ms. The method uses a coarse-to-fine training approach to prevent representation bloat while maintaining competitive novel-view synthesis performance with significantly fewer Gaussians (16K) compared to dense baselines.