SC-OmniGS: Self-Calibrating Omnidirectional Gaussian Splatting
Authors: Huajian Huang, Yingshu Chen, Longwei Li, Hui Cheng, Tristan Braud, Yajie Zhao, Sai-Kit Yeung
ICLR 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments have demonstrated that our proposed SC-Omni GS is able to recover a high-quality radiance field from noisy camera poses or even no pose prior in challenging scenarios characterized by wide baselines and non-object-centric configurations. The noticeable performance gain in the real-world dataset captured by consumer-grade omnidirectional cameras verifies the effectiveness of our general omnidirectional camera model in reducing the distortion of 360-degree images. |
| Researcher Affiliation | Academia | 1 The Hong Kong University of Science and Technology 2 Sun Yat-sen University 3 ICT, University of Southern California |
| Pseudocode | Yes | Algorithm 1 illustrates the backpropagation process and the usage of the proposed generic camera model. Algorithm 1: Differentiable Omnidirectional Camera Model |
| Open Source Code | No | The paper does not explicitly state that the source code for SC-Omni GS is being released, nor does it provide a direct link to a code repository for the described methodology. It only mentions using official published source codes for baselines. |
| Open Datasets | Yes | To verify the efficacy of SC-Omni GS, we conducted extensive experiments using a synthetic dataset Omni Blender (Choi et al., 2023) and a real-world 360Roam dataset (Huang et al., 2022). 360Roam (Huang et al., 2022) provides 360-degree captured images by a consumer-grade 360-degree camera for indoor scenes with multiple rooms, and corresponding initial sparse point clouds from Sf M. All data are under CC BY-NC-SA 4.0 license. Omni Blender (Choi et al., 2023) contains multi-view 360-degree images rendered from Blender synthetic single indoor scenes under MIT License. |
| Dataset Splits | Yes | We evaluated SG-Omni GS against several SOTA models on datasets of 360-degree images, including eight real-world multi-room scenes from 360Roam dataset (Huang et al., 2022) each with on average 110 training views and 37 test views, and three synthetic single-room scenes from Omni Blender dataset (Choi et al., 2023) each with 25 training views and 25 test views. |
| Hardware Specification | Yes | All methods were run on a desktop computer with an RTX 3090 GPU. On average, for a scene with a Ge Force RTX 3090 GPU, BARF trains for over 2 days, L2G-Ne RF and Cam P for half a day, 3D-GS (six-fold iterations), Omni GS and our SC-Omni GS within 30 minutes. |
| Software Dependencies | No | Our SC-Omni GS implementation is built on Pytorch and CUDA. While the software platforms are named, specific version numbers for PyTorch and CUDA are not provided, which are crucial for reproducibility. |
| Experiment Setup | Yes | The hyperparameters for 3D Gaussians optimization are set according to the default settings of 3D-GS (Kerbl et al., 2023), with λ = 0.2 and a total of 30,000 optimization iterations. We set the ratio threshold γ to 10. The omnidirectional camera model is shared across all views on individual scene. Moreover, we set the learning rate of the camera model Θ to 1e-4 and activate the angle distortion coefficients D using the Tanh function. For simplicity, we fix ft to 1. The initial learning rates for each camera quaternion q and translation t are set to 0.01, with exponential decay to 1.6e-4 and 6e-3, respectively, in 100 steps per camera. When calibrating from scratch, we increase the initial learning rate of t to 0.1. |