Look Gauss, No Pose: Novel View Synthesis using Gaussian Splatting without Accurate Pose Initialization (2410.08743v1)

Abstract: 3D Gaussian Splatting has recently emerged as a powerful tool for fast and accurate novel-view synthesis from a set of posed input images. However, like most novel-view synthesis approaches, it relies on accurate camera pose information, limiting its applicability in real-world scenarios where acquiring accurate camera poses can be challenging or even impossible. We propose an extension to the 3D Gaussian Splatting framework by optimizing the extrinsic camera parameters with respect to photometric residuals. We derive the analytical gradients and integrate their computation with the existing high-performance CUDA implementation. This enables downstream tasks such as 6-DoF camera pose estimation as well as joint reconstruction and camera refinement. In particular, we achieve rapid convergence and high accuracy for pose estimation on real-world scenes. Our method enables fast reconstruction of 3D scenes without requiring accurate pose information by jointly optimizing geometry and camera poses, while achieving state-of-the-art results in novel-view synthesis. Our approach is considerably faster to optimize than most competing methods, and several times faster in rendering. We show results on real-world scenes and complex trajectories through simulated environments, achieving state-of-the-art results on LLFF while reducing runtime by two to four times compared to the most efficient competing method. Source code will be available at https://github.com/Schmiddo/noposegs .

• The paper introduces a novel integration of camera pose optimization into the 3D Gaussian Splatting framework, eliminating the need for precise pose initialization.
• It employs analytical gradients for extrinsic parameters and a multi-layer optimization approach to jointly refine scene geometry and camera poses.
• Experiments on datasets like LLFF, Replica, and Tanks and Temples demonstrate state-of-the-art novel view synthesis performance with enhanced runtime efficiency.

Analysis of "Look Gauss, No Pose: Novel View Synthesis using Gaussian Splatting without Accurate Pose Initialization"

The paper presents an advancement in the domain of novel view synthesis (NVS), focusing on the limitations of current methodologies that heavily depend on precise camera pose information. The authors propose a method to address this dependency by integrating camera pose optimization directly into the 3D Gaussian Splatting (3DGS) framework.

Methodology Overview

At the core of their approach is the modification of the 3DGS framework, allowing simultaneous optimization of geometry and camera poses without requiring accurate initial pose estimations. The paper details the derivation of analytical gradients for extrinsic camera parameters, which are then seamlessly integrated into the high-performance CUDA rendering kernel of 3DGS. This integration facilitates new capabilities enabling not only enhanced pose estimation but also joint reconstruction and refinement tasks in 3D scenes.

Key Technical Contributions

1. Extension of Gaussian Splatting: By computing gradients for camera extrinsics, the authors extend the capabilities of Gaussian Splatting, enabling it to optimize camera poses within its rendering pipeline.
2. Multi-Layer Optimization Approach: The proposed framework allows for the joint optimization of scene representation and camera parameters with minimal assumptions about initial camera pose distribution.
3. Robustness and Efficiency Improvements: Enhancements such as an anisotropy loss term and adaptive thresholding for Gaussian pruning address issues like shape-radiance ambiguity, promoting more rapid convergence to high-fidelity solutions.
4. Real-World Applicability: Unlike traditional methods constrained by the need for accurate pose information, this technique demonstrates robustness across real-world datasets with inaccurate or entirely absent pose data.

Experimental Evaluation

The authors evaluated the method on several datasets, including LLFF, Replica, and Tanks and Temples. The results showcase state-of-the-art performance in novel-view synthesis and pose estimation. Runtime performance also exhibits significant enhancements, with reconstruction speeds claimed to be several times faster than competing methods.

Implications and Future Directions

The research illustrates a shift from traditional, pose-dependent NVS methodologies to more adaptive frameworks that can function under uncertain pose conditions. This flexibility is vital for applications in fields like robotics, augmented reality, and graphics, where precise pose estimates are often unavailable.

Future research may explore broader applications of this approach, such as its integration into SLAM systems or in contexts requiring real-time pose estimation. Further investigation into multi-hypothesis pose optimization or alternative Lie group parametrizations could potentially amplify performance gains.

Conclusion

In summary, this work contributes to the novel view synthesis landscape by alleviating the dependency on accurate pose information through a sophisticated integration into the Gaussian Splatting framework. The proposed method enhances both robustness and efficiency, marking a significant step forward for practical applications in dynamic and real-world environments.