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Found 7 papers, 6 papers with code
A Correct-and-Certify Approach to Self-Supervise Object Pose Estimators via Ensemble Self-Training
Real-world robotics applications demand object pose estimation methods that work reliably across a variety of scenarios.
Optimal and Robust Category-level Perception: Object Pose and Shape Estimation from 2D and 3D Semantic Keypoints
We consider an active shape model, where -- for an object category -- we are given a library of potential CAD models describing objects in that category, and we adopt a standard formulation where pose and shape are estimated from 2D or 3D keypoints via non-convex optimization.
Optimal Pose and Shape Estimation for Category-level 3D Object Perception
Our first contribution is to provide the first certifiably optimal solver for pose and shape estimation.
Kimera: from SLAM to Spatial Perception with 3D Dynamic Scene Graphs
This mental model captures geometric and semantic aspects of the scene, describes the environment at multiple levels of abstractions (e. g., objects, rooms, buildings), includes static and dynamic entities and their relations (e. g., a person is in a room at a given time).
ROBIN: a Graph-Theoretic Approach to Reject Outliers in Robust Estimation using Invariants
We also show that in practice the maximum k-core of the compatibility graph provides an approximation of the maximum clique, while being faster to compute in large problems.
3D Dynamic Scene Graphs: Actionable Spatial Perception with Places, Objects, and Humans
Our second contribution is to provide the first fully automatic Spatial PerceptIon eNgine(SPIN) to build a DSG from visual-inertial data.
TEASER: Fast and Certifiable Point Cloud Registration
We propose the first fast and certifiable algorithm for the registration of two sets of 3D points in the presence of large amounts of outlier correspondences.
