Trick-GS: A Balanced Bag of Tricks for Efficient Gaussian Splatting
Anil Armagan, Albert Sa\`a-Garriga, Bruno Manganelli, Mateusz Nowak,, Mehmet Kerim Yucel

TL;DR
Trick-GS introduces a combination of strategies to significantly improve the efficiency of Gaussian splatting for 3D reconstruction, enabling faster training, smaller models, and quicker rendering on resource-limited devices without sacrificing accuracy.
Contribution
It presents Trick-GS, a novel method that integrates progressive training, pruning, and acceleration techniques to enhance resource efficiency in Gaussian splatting.
Findings
Up to 2x faster training
40x smaller disk size
2x faster rendering speed
Abstract
Gaussian splatting (GS) for 3D reconstruction has become quite popular due to their fast training, inference speeds and high quality reconstruction. However, GS-based reconstructions generally consist of millions of Gaussians, which makes them hard to use on computationally constrained devices such as smartphones. In this paper, we first propose a principled analysis of advances in efficient GS methods. Then, we propose Trick-GS, which is a careful combination of several strategies including (1) progressive training with resolution, noise and Gaussian scales, (2) learning to prune and mask primitives and SH bands by their significance, and (3) accelerated GS training framework. Trick-GS takes a large step towards resource-constrained GS, where faster run-time, smaller and faster-convergence of models is of paramount concern. Our results on three datasets show that Trick-GS achieves up…
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Taxonomy
TopicsMineral Processing and Grinding
MethodsSPEED: Separable Pyramidal Pooling EncodEr-Decoder for Real-Time Monocular Depth Estimation on Low-Resource Settings
