TrackGS: Optimizing COLMAP-Free 3D Gaussian Splatting with Global Track Constraints (2502.19800v2)

Abstract: While 3D Gaussian Splatting (3DGS) has advanced ability on novel view synthesis, it still depends on accurate pre-computaed camera parameters, which are hard to obtain and prone to noise. Previous COLMAP-Free methods optimize camera poses using local constraints, but they often struggle in complex scenarios. To address this, we introduce TrackGS, which incorporates feature tracks to globally constrain multi-view geometry. We select the Gaussians associated with each track, which will be trained and rescaled to an infinitesimally small size to guarantee the spatial accuracy. We also propose minimizing both reprojection and backprojection errors for better geometric consistency. Moreover, by deriving the gradient of intrinsics, we unify camera parameter estimation with 3DGS training into a joint optimization framework, achieving SOTA performance on challenging datasets with severe camera movements.

• The paper proposes TrackGS, a method to train 3D Gaussian Splatting models by jointly optimizing scene and camera parameters from images, eliminating the need for COLMAP.
• Key innovations include theoretical derivation of intrinsic gradients and using global track information to guide Gaussian training for enhanced multi-view consistency.
• Experimental results demonstrate state-of-the-art performance in both camera parameter estimation and novel view synthesis, broadening applicability in AR/VR and real-time systems by removing pre-processing steps.

3D Gaussian Splatting Without Camera Parameters

In recent developments within the field of computer vision, the paradigm of 3D Gaussian Splatting (3DGS) offers a transformative approach for scene reconstruction and novel view synthesis. Traditional methodologies in 3DGS heavily rely on precise camera intrinsics and extrinsics, such as focal length and camera pose, which are typically pre-computed using tools like COLMAP. This dependency not only incurs additional computational costs but also limits adaptability in complex scenarios where accurate camera parameters may be challenging to determine. The research presented in "No Parameters, No Problem: 3D Gaussian Splatting without Camera Intrinsics and Extrinsics" provides an innovative solution by proposing a joint optimization framework to train 3DGS models purely from image collections without the requirement for any camera parameters.

Key Contributions and Methodological Advances

The paper introduces a comprehensive joint optimization method that integrates camera parameter estimation directly into the 3DGS training pipeline. Key innovations include:

1. Theoretical Derivation of Camera Intrinsics Gradients: A pivotal component of this work is the theoretical derivation of gradients for camera intrinsic parameters, particularly the focal length, which allows for concurrent optimization of these parameters during model training. This methodological advancement enables the training process to dynamically adjust camera intrinsics alongside model parameters through back-propagation.
2. Integration of Global Track Information: The paper employs global track information to guide the selection and training of 3D Gaussian kernels. By associating Gaussian kernels with specific image tracks, the approach ensures that these kernels are trained to approximate surface points more accurately, enhancing multi-view consistency and reducing reprojection errors.
3. Hybrid Training Strategy: The research presents a hybrid training strategy by combining standard 3DGS losses with multi-view geometric consistency and scale constraints, facilitating a cohesive optimization process for both camera parameters and Gaussian splatting.

Experimental Validation and Performance

The effectiveness of the proposed method is substantiated through extensive evaluations on public and synthetic datasets, including both real-world and controlled environments. The results demonstrate that this approach achieves state-of-the-art performance concerning camera parameter estimation and novel view synthesis, surpassing existing methodologies reliant on pre-computed camera parameters.

• Numerical Metrics: The proposed method shows superior rendering quality and accuracy in camera pose estimation, with significant improvements in standard metrics like PSNR, SSIM, and LPIPS across diverse scenes.
• Qualitative Performance: The method excels in rendering high-quality images, capturing intricate scene details, and maintaining consistency across widely varying camera angles, which were particularly apparent in scenes with complex camera movements and environments.

Implications and Future Directions

This research holds substantial implications for both theoretical and practical aspects of computer vision and graphics. From a theoretical perspective, it challenges the prevailing dependency on camera parameters, opening pathways for further exploration of parameter-independent models. Practically, the reduction in pre-processing requirements enhances the applicability of 3DGS in real-time and scalable applications, such as augmented reality and virtual reality environments, where dynamic adaptation to new scenes and viewpoints is critical.

Future research could focus on expanding the robustness of this approach across larger and more diverse datasets, incorporating additional priors or ancillary information to further refine camera parameter estimation under varying conditions. Additionally, adaptations for real-time processing and integration with neural rendering pipelines would be valuable extensions that align with the evolving demands of interactive and immersive media applications.

In summary, by decoupling 3DGS from traditional camera parameter dependencies, this research fundamentally shifts the approach to scene modeling and view synthesis, providing a blueprint for more adaptable and efficient methodologies in the domain of computer vision.