Relaxing Accurate Initialization Constraint for 3D Gaussian Splatting
Jaewoo Jung, Jisang Han, Honggyu An, Jiwon Kang, Seonghoon Park,, Seungryong Kim
TL;DR
RAIN-GS introduces a novel optimization strategy that relaxes the need for accurate initialization in 3D Gaussian splatting, enabling effective training from sub-optimal point clouds and maintaining high performance.
Contribution
The paper proposes RAIN-GS, a new method that allows 3D Gaussian splatting to be trained from noisy or randomly initialized point clouds, reducing reliance on precise SfM initialization.
Findings
RAIN-GS performs comparably or better than traditional methods with accurate initialization.
The approach effectively trains from noisy or random point clouds.
Quantitative and qualitative results validate the robustness of RAIN-GS.
Abstract
3D Gaussian splatting (3DGS) has recently demonstrated impressive capabilities in real-time novel view synthesis and 3D reconstruction. However, 3DGS heavily depends on the accurate initialization derived from Structure-from-Motion (SfM) methods. When the quality of the initial point cloud deteriorates, such as in the presence of noise or when using randomly initialized point cloud, 3DGS often undergoes large performance drops. To address this limitation, we propose a novel optimization strategy dubbed RAIN-GS (Relaing Accurate Initialization Constraint for 3D Gaussian Splatting). Our approach is based on an in-depth analysis of the original 3DGS optimization scheme and the analysis of the SfM initialization in the frequency domain. Leveraging simple modifications based on our analyses, RAIN-GS successfully trains 3D Gaussians from sub-optimal point cloud (e.g., randomly initialized…
Peer Reviews
Decision·Submitted to ICLR 2025
The authors propose certain modifications to the initialization steps prior to optimizing 3D Gaussian splatting representations. It leads to small but consistent improvements in the reconstruction quality on several datasets especially when the optimizer is started with noisy or random initialization of the 3D point coordinates. In general, the arguments and insights they present sounds reasonable in theory and seems to be helping in practice as shown in the empirical results.
The authors try to improve 3DGS performance when the Gaussians are initialized from random 3D points. While their results show some promise towards that end, they however assume that the camera poses are accurate. Typically, a full SfM pipeline must be executed to obtain accurate camera poses – must run bundle adjustment till completion. When running a full BA, accurate points are typically obtained as a by-product and no extra computation is needed. So, in other words, I don’t see a compelling
(1) The proposed method is simple to add to the existing 3DGS method (2) The authors provide exhaustive experiments to verify the effectiveness of the proposed method (3) The proposed method improves the original 3DGS clearly on the tanks-and-temples and the MipNeRF360 dataset.
(1) The ideas are intuitive and the contributions are not significant. (2) While I appreciate their improvements to the original 3DGS methods, there are more variants of 3DGS, and I wonder would the same strategies proposed in this paper can improve the other 3DGS methods(such as scaffold-gs, etc). (3) The method can improve 3DGS on the tanks-and-temples and MipNeRF360 dataset, I would like to see the effectiveness of this method on texture-less areas. And I would like to raise my score if the
1. This paper proposes to reconstruct Gaussian scenes using randomly initialized point clouds. In some cases, the proposed method surpasses Colmap+3DGS, which is impressive. 2. The proposed method encourages Gaussian scenes to first learn global structural information before focusing on detailed information. 3. The analysis in this paper is well-organized and clearly written.
1. The design in this paper mainly comes from experimental results. While the approach is simple and effective, it lacks theoretical novalities, and many hyperparameters require tuning. It would be beneficial for the authors to provide more detailed ablation experiments on these hyperparameters. 2. The paper assumes known multi-view image poses without point cloud reconstruction, but further analysis and explanation are needed regarding in which case this setting makes scene: - If the image pos
Code & Models
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsIndustrial Vision Systems and Defect Detection