Towards Better Robustness: Pose-Free 3D Gaussian Splatting for Arbitrarily Long Videos (2501.15096v2)

Abstract: 3D Gaussian Splatting (3DGS) has emerged as a powerful representation due to its efficiency and high-fidelity rendering. 3DGS training requires a known camera pose for each input view, typically obtained by Structure-from-Motion (SfM) pipelines. Pioneering works have attempted to relax this restriction but still face difficulties when handling long sequences with complex camera trajectories. In this paper, we propose Rob-GS, a robust framework to progressively estimate camera poses and optimize 3DGS for arbitrarily long video inputs. In particular, by leveraging the inherent continuity of videos, we design an adjacent pose tracking method to ensure stable pose estimation between consecutive frames. To handle arbitrarily long inputs, we propose a Gaussian visibility retention check strategy to adaptively split the video sequence into several segments and optimize them separately. Extensive experiments on Tanks and Temples, ScanNet, and a self-captured dataset show that Rob-GS outperforms the state-of-the-arts.

• The paper introduces Rob-GS, a framework that robustly learns joint pose and 3D Gaussian Splatting without relying on known camera poses.
• It employs an adjacent pose tracking method with optical flow matching to stabilize camera pose estimations between consecutive video frames.
• The adaptive segmentation strategy efficiently divides long sequences, significantly enhancing rendering quality and reducing training time.

Insights into Robust SfM-Free 3D Gaussian Splatting for Long Video Sequences

The paper "Towards Better Robustness: Progressively Joint Pose-3DGS Learning for Arbitrarily Long Videos" introduces Rob-GS, a novel framework designed to improve robustness in 3D Gaussian Splatting (3DGS) for arbitrarily long video sequences without relying on known camera poses. The paper focuses on overcoming the limitations of prior methods in scenarios involving extensive datasets and complex camera trajectories, which are common challenges in real-world applications of computer vision and graphics.

Key Contributions and Methodology

Rob-GS addresses two central issues in handling long video sequences: stable pose estimation and prevention of memory overflow. The proposed method introduces two critical innovations:

1. Adjacent Pose Tracking Method: This method leverages the continuity between frames in video sequences. By utilizing single-image-fitted Gaussians, Rob-GS ensures more stable camera pose estimations between consecutive frames. To enhance robustness, particularly in low-overlap conditions, the method incorporates optical flow matching with projection flow, stemming from depth maps and camera poses. This approach mitigates the inaccuracies often caused by heavily relying on photometric differences alone.
2. Adaptive Segmentation Strategy: To counteract the challenges posed by lengthy sequences, Rob-GS employs a "divide and conquer" approach that segments the video into manageable parts. The segmentation allows for local optimization of the 3DGS, maintaining rendering quality and computational efficiency. An adaptive mechanism ensures each segment captures a coherent scene portion, vital for large-scale scene reconstruction.

Results and Implications

The experimental evaluation of Rob-GS on the Tanks and Temples dataset and a self-collected dataset demonstrates its superior performance compared to state-of-the-art methods. The framework surpasses these baselines in terms of rendering quality, pose estimation accuracy, and training efficiency. Specifically, Rob-GS achieves a notable improvement in PSNR and SSIM metrics while reducing training times significantly compared to neural radiance field-based approaches like Nope-NeRF.

Theoretical and Practical Implications

Theoretically, the introduction of a robust SfM-free framework for 3D reconstruction marks a significant step forward in utilizing continuous video data for scene modeling. The use of Gaussian splatting provides a compelling alternative to traditional mesh or voxel-based representations, offering high-fidelity and real-time rendering capabilities, which are crucial for applications requiring immediate feedback.

Practically, Rob-GS opens new avenues for deploying 3D reconstruction in environments where obtaining calibrated camera parameters is infeasible, such as consumer-grade video capture devices or dynamically changing environments. This could enhance applications in virtual reality, augmented reality, and autonomous systems.

Future Directions

While Rob-GS sets a new benchmark, future developments could focus on integrating dynamic scene elements into the framework, broadening its applicability. Further advances in reducing dependency on depth estimation priors and enhancing the framework's resilience to rapid changes in lighting and motion will be areas of ongoing research. The robustness of Rob-GS in unconstrained scenarios positions it as a promising tool for advancing continuous 3D monitoring and interactive visual applications.

Overall, the work presents a comprehensive solution to an enduring problem in 3D visualization, with implications for both the development of new theoretical frameworks and the enhancement of practical applications in digital scene reconstruction.