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Found 8 papers, 2 papers with code
TerrainMesh: Metric-Semantic Terrain Reconstruction from Aerial Images Using Joint 2D-3D Learning
A local mesh is reconstructed using an initialization and refinement stage.
ELLIPSDF: Joint Object Pose and Shape Optimization with a Bi-level Ellipsoid and Signed Distance Function Description
It is compact because it relies on a low-dimensional latent representation of implicit object shape, allowing onboard storage of large multi-category object maps.
CORSAIR: Convolutional Object Retrieval and Symmetry-AIded Registration
This paper considers online object-level mapping using partial point-cloud observations obtained online in an unknown environment.
Fully Convolutional Geometric Features for Category-level Object Alignment
This paper focuses on pose registration of different object instances from the same category.
Localization and Mapping using Instance-specific Mesh Models
We show that the errors between projections of the mesh model and the observed keypoints and masks can be differentiated in order to obtain accurate instance-specific object shapes.
Mesh Reconstruction from Aerial Images for Outdoor Terrain Mapping Using Joint 2D-3D Learning
Each local mesh is initialized from sparse depth measurements.
OrcVIO: Object residual constrained Visual-Inertial Odometry
Introducing object-level semantic information into simultaneous localization and mapping (SLAM) system is critical.
Constructing Effective Personalized Policies Using Counterfactual Inference from Biased Data Sets with Many Features
This paper proposes a novel approach for constructing effective personalized policies when the observed data lacks counter-factual information, is biased and possesses many features.
