Fast Converging 3D Gaussian Splatting for 1-Minute Reconstruction

• URL: http://arxiv.org/abs/2601.19489v2
• Date: Wed, 28 Jan 2026 08:59:11 GMT
• Title: Fast Converging 3D Gaussian Splatting for 1-Minute Reconstruction
• Abstract summary: We present a fast 3DGS reconstruction pipeline designed to converge within one minute.<n>The pipeline was developed for the SIGGRAPH Asia 3DGS Fast Reconstruction Challenge.<n>Our method achieved the top performance with a PSNR of 28.43 and ranked first in the competition.
• Score: 21.22231699553299
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: We present a fast 3DGS reconstruction pipeline designed to converge within one minute, developed for the SIGGRAPH Asia 3DGS Fast Reconstruction Challenge. The challenge consists of an initial round using SLAM-generated camera poses (with noisy trajectories) and a final round using COLMAP poses (highly accurate). To robustly handle these heterogeneous settings, we develop a two-stage solution. In the first round, we use reverse per-Gaussian parallel optimization and compact forward splatting based on Taming-GS and Speedy-splat, load-balanced tiling, an anchor-based Neural-Gaussian representation enabling rapid convergence with fewer learnable parameters, initialization from monocular depth and partially from feed-forward 3DGS models, and a global pose refinement module for noisy SLAM trajectories. In the final round, the accurate COLMAP poses change the optimization landscape; we disable pose refinement, revert from Neural-Gaussians back to standard 3DGS to eliminate MLP inference overhead, introduce multi-view consistency-guided Gaussian splitting inspired by Fast-GS, and introduce a depth estimator to supervise the rendered depth. Together, these techniques enable high-fidelity reconstruction under a strict one-minute budget. Our method achieved the top performance with a PSNR of 28.43 and ranked first in the competition.

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