Search Results for author:
Found 7 papers, 6 papers with code
OpenStreetView-5M: The Many Roads to Global Visual Geolocation
Determining the location of an image anywhere on Earth is a complex visual task, which makes it particularly relevant for evaluating computer vision algorithms.
BOP Challenge 2023 on Detection, Segmentation and Pose Estimation of Seen and Unseen Rigid Objects
In the new tasks, methods were required to learn new objects during a short onboarding stage (max 5 minutes, 1 GPU) from provided 3D object models.
GigaPose: Fast and Robust Novel Object Pose Estimation via One Correspondence
We present GigaPose, a fast, robust, and accurate method for CAD-based novel object pose estimation in RGB images.
CNOS: A Strong Baseline for CAD-based Novel Object Segmentation
We propose a simple three-stage approach to segment unseen objects in RGB images using their CAD models.
NOPE: Novel Object Pose Estimation from a Single Image
The practicality of 3D object pose estimation remains limited for many applications due to the need for prior knowledge of a 3D model and a training period for new objects.
PIZZA: A Powerful Image-only Zero-Shot Zero-CAD Approach to 6 DoF Tracking
Our results on challenging datasets are on par with previous works that require much more information (training images of the target objects, 3D models, and/or depth data).
Templates for 3D Object Pose Estimation Revisited: Generalization to New Objects and Robustness to Occlusions
It relies on a small set of training objects to learn local object representations, which allow us to locally match the input image to a set of "templates", rendered images of the CAD models for the new objects.
