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Found 9 papers, 1 papers with code
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.
Rotational Subgroup Voting and Pose Clustering for Robust 3D Object Recognition
The key insight of our work is that a single correspondence between oriented points on the two models is constrained to cast votes in a 1 DoF rotational subgroup of the full group of poses, SE(3).
In search of inliers: 3d correspondence by local and global voting
The novelty of our method lies in the use of a combination of local and global constraints to determine if a vote should be cast.
Pose Estimation using Local Structure-Specific Shape and Appearance Context
We address the problem of estimating the alignment pose between two models using structure-specific local descriptors.
PointVoteNet: Accurate Object Detection and 6 DoF Pose Estimation in Point Clouds
We present a learning-based method for 6 DoF pose estimation of rigid objects in point cloud data.
Bridging the Reality Gap for Pose Estimation Networks using Sensor-Based Domain Randomization
While the use of synthetic training data prevents the need for manual annotation, there is currently a large performance gap between methods trained on real and synthetic data.
SurfEmb: Dense and Continuous Correspondence Distributions for Object Pose Estimation with Learnt Surface Embeddings
We present an approach to learn dense, continuous 2D-3D correspondence distributions over the surface of objects from data with no prior knowledge of visual ambiguities like symmetry.
ParaPose: Parameter and Domain Randomization Optimization for Pose Estimation using Synthetic Data
The use of synthetic training data avoids this data collection problem, but a configuration of the training procedure is necessary to overcome the domain gap problem.
Ki-Pode: Keypoint-based Implicit Pose Distribution Estimation of Rigid Objects
However, a single estimate is unable to express visual ambiguity, which in many cases is unavoidable due to object symmetries or occlusion of identifying features.
