Improving Robustness for Joint Optimization of Camera Poses and Decomposed Low-Rank Tensorial Radiance Fields (2402.13252v1)

Abstract: In this paper, we propose an algorithm that allows joint refinement of camera pose and scene geometry represented by decomposed low-rank tensor, using only 2D images as supervision. First, we conduct a pilot study based on a 1D signal and relate our findings to 3D scenarios, where the naive joint pose optimization on voxel-based NeRFs can easily lead to sub-optimal solutions. Moreover, based on the analysis of the frequency spectrum, we propose to apply convolutional Gaussian filters on 2D and 3D radiance fields for a coarse-to-fine training schedule that enables joint camera pose optimization. Leveraging the decomposition property in decomposed low-rank tensor, our method achieves an equivalent effect to brute-force 3D convolution with only incurring little computational overhead. To further improve the robustness and stability of joint optimization, we also propose techniques of smoothed 2D supervision, randomly scaled kernel parameters, and edge-guided loss mask. Extensive quantitative and qualitative evaluations demonstrate that our proposed framework achieves superior performance in novel view synthesis as well as rapid convergence for optimization.

• The paper presents a novel spectral filtering approach that enables robust joint optimization of camera poses and 3D scene geometry.
• It leverages separable component-wise convolution and edge-guided loss masks to reduce training iterations from 200k to 50k.
• The method improves memory efficiency and computation speed, advancing 3D reconstruction in AR, VR, and robotics applications.

Improving Camera Pose Optimization with Decomposed Low-Rank Tensorial Radiance Fields through Spectral Filtering

Joint Optimization Challenges with Decomposed Tensorial Radiance Fields

The task of joint optimization of camera poses and 3D scene geometry using only 2D image supervision presents a significant challenge in the field of neural rendering and 3D reconstruction. The literature has extensively explored techniques like Neural Radiance Fields (NeRF) and their voxel-based counterparts, showcasing remarkable novel view synthesis quality. However, these methods often struggle with computational inefficiencies and memory-intensive requirements, particularly in maintaining a dense 3D voxel grid. Recent advancements, such as decomposed low-rank tensor methods (e.g., TensoRF), have made strides in addressing these issues by offering a significant reduction in memory use and computational demands without sacrificing performance. Yet, when it comes to joint optimization -- refining camera poses and 3D scene geometry simultaneously -- these methods can fall short, often getting trapped in local optima due to their lack of control over the underlying spectral properties of the 3D scene representation.

Our Contributions and Novel Approach

In our paper, we introduce a novel framework that enhances the robustness and stability of the joint optimization process for camera poses and decomposed low-rank tensorial radiance fields. The core innovation lies in our application of specially designed spectral filters that enable efficient control over the spectrum of the radiance field, along with our efficient 3D filtering method leveraging separable component-wise convolution. Our approach not only mitigates the problem of getting trapped in local optima but also significantly speeds up the convergence of the optimization process, as evidenced by our method requiring only 50k training iterations compared to the 200k typically needed by previous methods.

Our primary contributions are three-fold:

• We propose a novel learning strategy grounded in spectral control through the application of convolutional Gaussian filters, enabling more effective joint optimization of camera poses and 3D scene geometry.
• We introduce techniques for increasing the robustness of the optimization process, including smoothed 2D supervision, randomly scaled kernel parameters, and an edge-guided loss mask.
• We demonstrate through extensive evaluation that our framework not only achieves superior performance in novel view synthesis but also exhibits rapid convergence, with training time reduced to 25% of that required by existing methods.

Theoretical and Practical Implications

Our research presents both theoretical and practical advancements in the field of 3D scene reconstruction and neural rendering. By addressing the challenges of joint optimization with decomposed low-rank tensorial radiance fields, we offer insights into the critical role of spectral properties and the benefits of spectral filtering. This opens up new avenues for future exploration in improving the efficiency, robustness, and quality of 3D scene reconstruction methods. Practically, our work has significant implications for applications relying on accurate 3D scene representations and camera pose estimations, such as augmented reality (AR), virtual reality (VR), and robotics.

Speculations on Future Developments

Looking ahead, we anticipate further research to build upon our findings, exploring additional spectral filtering techniques and their impact on joint optimization. There's also potential for integrating our approach with other forms of tensor decomposition and neural rendering architectures, potentially unlocking even greater efficiencies and performance improvements. Moreover, as computational resources continue to evolve, the scalability of methods like ours will become increasingly critical, paving the way for more complex and detailed 3D scene reconstructions in real-time applications.

In conclusion, our work represents a significant step forward in the joint optimization of camera poses and 3D scene geometry, offering a robust, efficient, and theoretically grounded framework that advances the state-of-the-art in neural rendering and 3D reconstruction.