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Found 8 papers, 1 papers with code
Globally Optimal and Efficient Vanishing Point Estimation in Atlanta World
In Atlanta world, given a set of image lines, we aim to cluster them by the unknown-but-sought VPs whose number is unknown.
Efficient and Robust Point Cloud Registration via Heuristics-guided Parameter Search
Our strategy largely reduces the search space and can guarantee accuracy with only a few inlier samples, therefore enjoying an excellent trade-off between efficiency and robustness.
Learning Accurate 3D Shape Based on Stereo Polarimetric Imaging
As to the orthographic projection problem, we propose a novel Viewing Direction-aided Positional Encoding (VDPE) strategy.
DDIT: Semantic Scene Completion via Deformable Deep Implicit Templates
It abstracts the shape prior of a category, and thus can provide constraints on the overall shape of an instance.
Deterministic Point Cloud Registration via Novel Transformation Decomposition
Given a set of putative 3D-3D point correspondences, we aim to remove outliers and estimate rigid transformation with 6 degrees of freedom (DOF).
Learning To Identify Correct 2D-2D Line Correspondences on Sphere
In contrast, we propose the first approach suitable for both structured and unstructured scenes.
Learning Icosahedral Spherical Probability Map Based on Bingham Mixture Model for Vanishing Point Estimation
Based on this map, we can detect all the VPs.
Quasi-Globally Optimal and Efficient Vanishing Point Estimation in Manhattan World
In Manhattan world, given several lines in a calibrated image, we aim at clustering them by three unknown-but-sought VPs.
