Sparse-view Pose Estimation and Reconstruction via Analysis by Generative Synthesis (2412.03570v1)

Abstract: Inferring the 3D structure underlying a set of multi-view images typically requires solving two co-dependent tasks -- accurate 3D reconstruction requires precise camera poses, and predicting camera poses relies on (implicitly or explicitly) modeling the underlying 3D. The classical framework of analysis by synthesis casts this inference as a joint optimization seeking to explain the observed pixels, and recent instantiations learn expressive 3D representations (e.g., Neural Fields) with gradient-descent-based pose refinement of initial pose estimates. However, given a sparse set of observed views, the observations may not provide sufficient direct evidence to obtain complete and accurate 3D. Moreover, large errors in pose estimation may not be easily corrected and can further degrade the inferred 3D. To allow robust 3D reconstruction and pose estimation in this challenging setup, we propose SparseAGS, a method that adapts this analysis-by-synthesis approach by: a) including novel-view-synthesis-based generative priors in conjunction with photometric objectives to improve the quality of the inferred 3D, and b) explicitly reasoning about outliers and using a discrete search with a continuous optimization-based strategy to correct them. We validate our framework across real-world and synthetic datasets in combination with several off-the-shelf pose estimation systems as initialization. We find that it significantly improves the base systems' pose accuracy while yielding high-quality 3D reconstructions that outperform the results from current multi-view reconstruction baselines.

• The paper introduces SparseAGS, a framework that leverages diffusion-model generative priors with analysis-by-synthesis for enhanced 3D reconstruction and pose estimation.
• It refines initial pose estimates through a joint optimization strategy and robust error correction without relying on precise camera poses.
• Results show significant improvements in rotation and camera center accuracy on both real and synthetic datasets, expanding its practical applications.

Sparse-view Pose Estimation and Reconstruction via Analysis by Generative Synthesis

The paper presents SparseAGS, a novel framework designed to advance 3D reconstruction and pose estimation using sparse-view images. The significance of this work lies in its approach to overcoming challenges associated with sparse-view 3D inference, where limited image data often complicates accurate 3D structure and camera pose estimation. The framework leverages an "analysis by synthesis" method, enhanced by novel-view-synthesis-based generative priors, to simultaneously improve the quality of 3D reconstruction and camera pose estimation.

Methodology

SparseAGS builds upon the traditional analysis-by-synthesis concept by integrating generative priors from diffusion models. This is achieved through a joint optimization procedure that refines initial pose estimates from various off-the-shelf pose estimation systems. A prominent distinction of SparseAGS from existing methods is its ability to operate without pre-assumed precise camera poses—a typical requirement in state-of-the-art 3D reconstruction techniques. The framework employs a 6-DoF novel-view generative prior, enabling more robust 3D inference in real-world scenarios where the input data might not fully capture the object. Additionally, considerations for outliers and corrections through a combination of a discrete search and continuous optimization strategies further enhance the robustness and efficacy of this approach.

Results

The framework's performance is validated across real-world and synthetic datasets, consistently outperforming baseline methods. SparseAGS demonstrates substantial improvements in 3D reconstruction and pose accuracy, particularly when utilizing generative priors to address errors in initial camera pose estimates. The quantitative improvements are observed in metrics such as rotation and camera center accuracy, wherein SparseAGS consistently outperforms prior methods, such as SPARF, which often suffer from reliance on correspondences that can be inaccurate with large viewpoint changes.

Implications and Future Research

The research contributes significantly to sparse-view 3D reconstruction and pose estimation disciplines by bridging the gap between pose initialization and accurate 3D reconstruction using limited data. The generative synthesis approach adopted here suggests promising avenues for enhancing robust 3D reconstruction in scenarios where comprehensive datasets are unfeasible.

Practically, the proposed method holds potential applications in areas requiring rapid and accurate 3D scene understanding from limited perspectives, such as autonomous navigation, augmented reality, and robotics. Theoretically, it underscores the benefits of generative models in dealing with incomplete or partial observation data, hinting at future developments in AI where generative models could play a central role in improving inference capabilities under sparse conditions.

Future developments could involve extending SparseAGS to broader scene contexts beyond object-centric settings, incorporating handling of occlusions and truncations, which remain as limitations in the proposed framework. Additionally, further exploration into fine-tuning generative models for diverse real-world scenarios could enhance the adaptability and accuracy of generative priors in complex environments.

In conclusion, SparseAGS provides a compelling framework that leverages generative models for enhancing sparse-view 3D reconstruction and pose estimation, offering substantial improvements over traditional methods and setting a foundation for subsequent research in sparse-data-driven 3D inference.