Generalizable Single-view Object Pose Estimation by Two-side Generating and Matching

In this paper, we present a novel generalizable object pose estimation method to determine the object pose using only one RGB image. Unlike traditional approaches that rely on instance-level object pose estimation and necessitate extensive training data, our method offers generalization to unseen objects without extensive training, operates with a single reference image of the object, and eliminates the need for 3D object models or multiple views of the object. These characteristics are achieved by utilizing a diffusion model to generate novel-view images and conducting a two-sided matching on these generated images. Quantitative experiments demonstrate the superiority of our method over existing pose estimation techniques across both synthetic and real-world datasets. Remarkably, our approach maintains strong performance even in scenarios with significant viewpoint changes, highlighting its robustness and versatility in challenging conditions. The code will be re leased at https://github.com/scy639/Gen2SM.

Extreme Two-View Geometry From Object Poses with Diffusion Models

Human has an incredible ability to effortlessly perceive the viewpoint difference between two images containing the same object, even when the viewpoint change is astonishingly vast with no co-visible regions in the images. This remarkable skill, however, has proven to be a challenge for existing camera pose estimation methods, which often fail when faced with large viewpoint differences due to the lack of overlapping local features for matching. In this paper, we aim to effectively harness the power of object priors to accurately determine two-view geometry in the face of extreme viewpoint changes. In our method, we first mathematically transform the relative camera pose estimation problem to an object pose estimation problem. Then, to estimate the object pose, we utilize the object priors learned from a diffusion model Zero123 to synthesize novel-view images of the object. The novel-view images are matched to determine the object pose and thus the two-view camera pose. In experiments, our method has demonstrated extraordinary robustness and resilience to large viewpoint changes, consistently estimating two-view poses with exceptional generalization ability across both synthetic and real-world datasets. Code will be available at https://github.com/scy639/Extreme-Two-View-Geometry-From-Object-Poses-with-Diffusion-Models.