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Found 6 papers, 3 papers with code
NSLF-OL: Online Learning of Neural Surface Light Fields alongside Real-time Incremental 3D Reconstruction
This limits the usage of related models in the robotics community for 3D reconstruction since robots (1) usually only capture a very small range of view directions to surfaces that cause arbitrary predictions on unseen, novel direction, (2) requires real-time algorithms, and (3) work with growing scenes, e. g., in robotic exploration.
Uni-Fusion: Universal Continuous Mapping
Based on this, our framework divides the point cloud into regular grid voxels and generates a latent feature in each voxel to form a Latent Implicit Map (LIM) for geometries and arbitrary properties.
An Algorithm for the SE(3)-Transformation on Neural Implicit Maps for Remapping Functions
As our neural implicit map is transformable, our model supports remapping for this special map of latent features.
Self-supervised Point Set Local Descriptors for Point Cloud Registration
In this work, we propose to learn local descriptors for point clouds in a self-supervised manner.
Robotics
Non-iterative One-step Solution for Point Set Registration Problem on Pose Estimation without Correspondence
In this work, we propose to directly find the one-step solution for the point set registration problem without correspondences.
Robotics
Area Graph: Generation of Topological Maps using the Voronoi Diagram
Representing a scanned map of the real environment as a topological structure is an important research topic in robotics.
Robotics
