Search Results for author:
Found 20 papers, 12 papers with code
BOP Challenge 2022 on Detection, Segmentation and Pose Estimation of Specific Rigid Objects
In 2022, we witnessed another significant improvement in the pose estimation accuracy -- the state of the art, which was 56. 9 AR$_C$ in 2019 (Vidal et al.) and 69. 8 AR$_C$ in 2020 (CosyPose), moved to new heights of 83. 7 AR$_C$ (GDRNPP).
Map-free Visual Relocalization: Metric Pose Relative to a Single Image
Can we relocalize in a scene represented by a single reference image?
On the Limits of Pseudo Ground Truth in Visual Camera Re-localisation
This begs the question whether the choice of the reference algorithm favours a certain family of re-localisation methods.
Cuboids Revisited: Learning Robust 3D Shape Fitting to Single RGB Images
A RANSAC estimator guided by a neural network fits these primitives to 3D features, such as a depth map.
Visual Camera Re-Localization Using Graph Neural Networks and Relative Pose Supervision
Visual re-localization means using a single image as input to estimate the camera's location and orientation relative to a pre-recorded environment.
BOP Challenge 2020 on 6D Object Localization
This paper presents the evaluation methodology, datasets, and results of the BOP Challenge 2020, the third in a series of public competitions organized with the goal to capture the status quo in the field of 6D object pose estimation from an RGB-D image.
Visual Camera Re-Localization from RGB and RGB-D Images Using DSAC
The framework consists of a deep neural network and fully differentiable pose optimization.
CONSAC: Robust Multi-Model Fitting by Conditional Sample Consensus
We present a robust estimator for fitting multiple parametric models of the same form to noisy measurements.
Reinforced Feature Points: Optimizing Feature Detection and Description for a High-Level Task
We address a core problem of computer vision: Detection and description of 2D feature points for image matching.
Expert Sample Consensus Applied to Camera Re-Localization
In this work, we fit the 6D camera pose to a set of noisy correspondences between the 2D input image and a known 3D environment.
Neural-Guided RANSAC: Learning Where to Sample Model Hypotheses
In contrast, we learn hypothesis search in a principled fashion that lets us optimize an arbitrary task loss during training, leading to large improvements on classic computer vision tasks.
Ranked #1 on
Horizon Line Estimation
on Horizon Lines in the Wild
BOP: Benchmark for 6D Object Pose Estimation
We propose a benchmark for 6D pose estimation of a rigid object from a single RGB-D input image.
iPose: Instance-Aware 6D Pose Estimation of Partly Occluded Objects
We address the task of 6D pose estimation of known rigid objects from single input images in scenarios where the objects are partly occluded.
Learning Less is More - 6D Camera Localization via 3D Surface Regression
Popular research areas like autonomous driving and augmented reality have renewed the interest in image-based camera localization.
PoseAgent: Budget-Constrained 6D Object Pose Estimation via Reinforcement Learning
In this work we propose to learn an efficient algorithm for the task of 6D object pose estimation.
Global Hypothesis Generation for 6D Object Pose Estimation
Most modern approaches solve this task in three steps: i) Compute local features; ii) Generate a pool of pose-hypotheses; iii) Select and refine a pose from the pool.
DSAC - Differentiable RANSAC for Camera Localization
The most promising approach is inspired by reinforcement learning, namely to replace the deterministic hypothesis selection by a probabilistic selection for which we can derive the expected loss w. r. t.
Random Forests versus Neural Networks - What's Best for Camera Localization?
This work addresses the task of camera localization in a known 3D scene given a single input RGB image.
Uncertainty-Driven 6D Pose Estimation of Objects and Scenes From a Single RGB Image
In recent years, the task of estimating the 6D pose of object instances and complete scenes, i. e. camera localization, from a single input image has received considerable attention.
Learning Analysis-by-Synthesis for 6D Pose Estimation in RGB-D Images
This is done by describing the posterior density of a particular object pose with a convolutional neural network (CNN) that compares an observed and rendered image.
