Search Results for author:
Found 8 papers, 6 papers with code
BOP Challenge 2024 on Model-Based and Model-Free 6D Object Pose Estimation
We present the evaluation methodology, datasets and results of the BOP Challenge 2024, the 6th in a series of public competitions organized to capture the state of the art in 6D object pose estimation and related tasks.
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.
Ranked #2 on
Photo geolocation estimation
on OpenStreetView-5M
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.
Ranked #2 on
6D Pose Estimation
on DTTD-Mobile
(AR CoU metric)
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.
