Extreme Two-View Geometry From Object Poses with Diffusion Models

Introduction to Extreme Two-View Geometry Estimation with Diffusion Models

The problem of relative camera pose estimation serves as a foundational pillar in computer vision with applications ranging from augmented reality to 3D reconstruction. However, estimating poses between two views with no co-visible regions remains a significant challenge. The prevalent local feature matching techniques falter in scenarios with large viewpoint changes, limited texture information, or sparse environments. Addressing this challenge could lead to monumental advancements within the computer vision field.

In conventional settings, pose estimation algorithms leverage featural correspondences or dense view-based approaches with various types of priors such as category-specific knowledge. Despite some novel strategies integrating transformer architectures for object pose estimation, their generalization across diverse domains is still lacking. To tackle this, researchers have pivoted towards leveraging diffusion models that learn robust object priors, encoded during training on massive image datasets.

Diffusion Models as a Solution

Harnessing diffusion models offers a promising alternative for improving the generalization ability of pose estimation. These models, such as Zero123, are adept at synthesizing novel-view images, which are then used to refine pose estimation. However, their reliance on an input image looking directly at the object, and the implicitly defined canonical object coordinate within them, present serious challenges—especially when aligning input images to this canonical object system. Overcoming this requires detailed understanding of the geometry of the input images relative to a presumed object-centric camera perspective.

Methodology

The authors propose a novel framework that first redefines the two-view pose estimation problem into one of object pose estimation. By utilizing the Zero123 model to produce multiple images from varying viewpoints, the algorithm can then match these novel views with a second input image to determine plausible object poses. This estimation is then transformed back into the conventional two-view camera pose context. The strength of this approach lies in its superior performance over existing methods under the challenge of extreme viewpoint variations.

Experimental Results and Impact

Empirical evaluation demonstrates that this novel framework decisively outperforms competing methods on both synthetic and real-world data sets with a considerable margin. This method achieves higher accuracy in predicting relative camera poses despite significant viewpoint changes and exhibits robust generalization when applied to out-of-distribution datasets. The improved accuracy in extreme two-view pose estimation illustrates the method's potential to elevate tasks such as visual odometry and other computer vision applications, ultimately pushing the boundaries of what's achievable with current technology.

Closing Remarks

The integration of diffusion models into pose estimation is an innovative direction that capitalizes on the powerful image synthesis capabilities of these models. By transforming the pose estimation problem and judiciously using generated novel-view images, the framework provides a compelling solution to the longstanding issue of pose estimation from extreme viewpoints. Extending the work to explore the applications of this framework in practice, especially in scenarios where other methods falter, will be a significant stride forward for computer vision.