Point-set registration
In computer vision, pattern recognition, and robotics, point-set registration, also known as point-cloud registration or scan matching, is the process of finding a spatial transformation (e.g., scaling, rotation and translation) that aligns two point clouds. The purpose of finding such a transformation includes merging multiple data sets into a globally consistent model (or coordinate frame), and mapping a new measurement to a known data set to identify features or to estimate its pose. Raw 3D point cloud data are typically obtained from Lidars and RGB-D cameras. 3D point clouds can also be generated from computer vision algorithms such as triangulation, bundle adjustment, and more recently, monocular image depth estimation using deep learning. For 2D point set registration used in image processing and feature-based image registration, a point set may be 2D pixel coordinates obtained by feature extraction from an image, for example corner detection. Point cloud registration has extensive applications in autonomous driving, motion estimation and 3D reconstruction, object detection and pose estimation, robotic manipulation, simultaneous localization and mapping (SLAM), panorama stitching, virtual and augmented reality, and medical imaging.
Point set registration is the process of aligning two point sets. Here, the blue fish is being registered to the red fish. In computer vision, pattern recognition, and robotics, point-set registration, also known as point-cloud registration or scan matching, is the process of finding a spatial transformation (e.g., scaling, rotation and translation) that aligns two point clouds. The purpose of finding such a transformation includes merging multiple data sets into a globally consistent model (or coordinate frame), and mapping a new measurement to a known data set to identify features or to…
saved by
related reading
- PointNetstanford.edu
- Perspective-n-Point - Wikipediaen.wikipedia.org
- 2409.15615arxiv.org
- DUSt3R: Geometric 3D Vision Made Easyarxiv.org
- DreamGaussian: Generative Gaussian Splatting for Efficient 3D Content Creation | PDFarxiv.org
- Box2Mask: Weakly Supervised 3D Semantic Instance Segmentation Using Bounding Boxesvirtualhumans.mpi-inf.mpg.de
- 3D Object Detection via 2D Segmentation-Based Computational Integral Imaging Applied to a Real Videoncbi.nlm.nih.gov
- 3D-Aware Ellipse Prediction for Object-Based Camera Pose Estimationarxiv.org
- S4Darxiv.org
- Amodal Detection of 3D Objects: Inferring 3D Bounding Boxes from 2D Ones in RGB-Depth Imagescis.temple.edu
- Regularized by Score Matching (LeCun)papers.nips.cc
- Enhancing 3D Object Detection with 2D Detection-Guided Query Anchorsarxiv.org