TinySplat: Feedforward Approach for Generating Compact 3D Scene Representation
Zetian Song, Jiaye Fu, Jiaqi Zhang, Xiaohan Lu, Chuanmin Jia, Siwei Ma, Wen Gao

TL;DR
TinySplat introduces a feedforward method that significantly compresses 3D scene representations, reducing storage by over 100 times while maintaining quality, through a novel, training-free compression framework.
Contribution
It presents a new training-free compression framework for 3D Gaussian Splatting that systematically reduces redundancy in geometric, perceptual, and spatial domains.
Findings
Achieves over 100x compression of 3D Gaussian data.
Maintains comparable quality with only 6% of the original storage.
Requires only 25% of encoding time and 1% of decoding time.
Abstract
The recent development of feedforward 3D Gaussian Splatting (3DGS) presents a new paradigm to reconstruct 3D scenes. Using neural networks trained on large-scale multi-view datasets, it can directly infer 3DGS representations from sparse input views. Although the feedforward approach achieves high reconstruction speed, it still suffers from the substantial storage cost of 3D Gaussians. Existing 3DGS compression methods relying on scene-wise optimization are not applicable due to architectural incompatibilities. To overcome this limitation, we propose TinySplat, a complete feedforward approach for generating compact 3D scene representations. Built upon standard feedforward 3DGS methods, TinySplat integrates a training-free compression framework that systematically eliminates key sources of redundancy. Specifically, we introduce View-Projection Transformation (VPT) to reduce geometric…
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Taxonomy
TopicsAdvanced Vision and Imaging · 3D Shape Modeling and Analysis · Generative Adversarial Networks and Image Synthesis
