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arXiv Machine Learning

GaussianRenderFormer: Transformer-based Rendering of 3D Gaussian Splats

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arXiv Computer Vision
Aug 28

ReSplat: Learning Recurrent Gaussian Splatting

ReSplat introduces a recurrent Gaussian splatting model that iteratively refines 3D Gaussians using the rendering error as a feedback signal, avoiding explicit gradient computation. The method starts from a compact reconstruction in a subsampled space, producing far fewer Gaussians than prior per‑pixel models, which reduces computational cost. Experiments on multiple datasets, view counts, and resolutions show state‑of‑the‑art performance with faster rendering speeds.

By Haofei Xu, Daniel Barath, Andreas Geiger, Marc Pollefeys
benchmarks
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arXiv Computer Vision
Sep 28

Gauss What You Need: Compact Gaussian Splatting Across Scene Scales

Gauss What You Need: Compact Gaussian Splatting Across Scene Scales introduces TangoGS, a method that automatically selects the number of Gaussian primitives for 3D Gaussian Splatting by combining capture-derived model sizing with training-based adaptation. The approach first estimates a learning allowance based on the capture’s total pixels, then adjusts the number of Gaussians during training according to reconstruction quality. On standard benchmarks, TangoGS matches the best baseline’s PSNR while using 48% fewer Gaussians, and on larger captures it scales automatically to achieve the highest mean PSNR with 2.3× more Gaussians.

By Afif Boudaoud, Jiayi Liu, Alexandru Calotoiu, Torsten Hoefler
benchmarks
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arXiv Computer Vision
Aug 28

KISS-GS: 3D Gaussian Splatting Compression Kept Simple

KISS-GS is a modular compression pipeline for 3D Gaussian Splatting (3DGS) scenes that separates compression from training. It first compacts a vanilla 3DGS scene by 15.7× using state‑of‑the‑art pruning, then encodes the result into the SOG‑XT image‑based format, achieving an additional 6.6× reduction. Optional encoding‑aware fine‑tuning can further cut the size by 2.2×, yielding total reductions of 85× to 319× on standard benchmarks while enabling web‑native decoding.

By Wieland Morgenstern, Friedrich Elias Branschke, Florian Fleischmann, Adrian Szatmari, Paul Schlack, Florian Barthel, Peter Eisert, Anna Hilsmann
fine-tuningefficiencybenchmarks
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arXiv Computer Vision
Sep 4

F4Splat: Feed-Forward Predictive Densification for Feed-Forward 3D Gaussian Splatting

F4Splat introduces a feed‑forward predictive densification strategy for 3D Gaussian splatting that allocates Gaussians based on a densification‑score guided by spatial complexity and multi‑view overlap. The method predicts per‑region scores to estimate required Gaussian density, enabling explicit control over the total Gaussian budget without retraining. This adaptive allocation reduces redundancy in simple regions and minimizes duplicate Gaussians across overlapping views, yielding compact yet high‑quality 3D representations and superior novel‑view synthesis performance with fewer Gaussians.

By Injae Kim, Chaehyeon Kim, Minseong Bae, Minseok Joo, Hyunwoo J. Kim
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arXiv Computer Vision
Sep 25

Only What Was Seen: Observation-Gram Compaction of View-Dependent Appearance in 3D Gaussian Splatting

The paper introduces an observation‑Gram matrix that captures how each Gaussian in a 3D Gaussian Splatting model is viewed from training camera directions. This matrix serves as a distortion metric, enabling closed‑form degree reduction, Lagrangian rate‑distortion degree allocation, and matrix‑weighted vector quantisation. When applied to the Compressed3D framework, the metric improves PSNR by 0.49 dB before fine‑tuning and still yields a 0.32 dB gain at matched bitrate without any training images, while a training‑free stack built on the metric is 15% smaller than the image‑free GSICO at equal quality on Mip‑NeRF 360.

By Krzysztof Pietroszek
fine-tuning
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