The practical applications of 3D model acquisition are manifold. In this paper, we present our RGB-D SLAM system, i.e., an approach to generate colored 3D models of objects and indoor scenes using the hand-held Microsoft Kinect sensor. Our approach consists of four processing steps as illustrated in Figure 1. First, we extract SURF features from the incoming color images.
A Real-Time Infrared Stereo Matching Algorithm for RGB-D Low-cost, commercial RGB-D cameras have become one of the main sensors for indoor scene 3D perception and robot navigation and localization. In these studies, the Intel RealSense R200 sensor (R200) is popular among many researchers, but its integrated commercial stereo matching algorithm has a small detection range, short measurement distance and low depth map resolution, which severely
CiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda):The modeling of three-dimensional scene geometry from temporal point cloud streams is of particular interest for a variety of computer vision applications. With the ad-vent of RGB-D imaging devices that deliver dense, metric and textured 6-D data in real-time, on-the-fly reconstruc-tion of static environments has come
Mobile3DRecon:Real-time Monocular 3D With an RGB-D camera, a real-time  proposed a real-time 3D reconstruction pipeline on PC, which utilizes visibility-based and condence-based fusion for merging multiple depth maps to online large-scale 3D model. Pollefeys et al.  presented a complete sys-
REAL-TIME CAPTURE AND RENDERING OF PHYSICAL by RGB-D camera systems. First, I propose a novel extrinsic calibration algorithm that can accurately and rapidly calibrate the geometric relationships across an arbi-trary number of RGB-D cameras on a network. Second, I propose a novel rendering pipeline that can capture and render, in real-time, dynamic scenes in the presence of arbitrary
Overview. RTAB-Map (Real-Time Appearance-Based Mapping) is a RGB-D, Stereo and Lidar Graph-Based SLAM approach based on an incremental appearance-based loop closure detector. The loop closure detector uses a bag-of-words approach to determinate how likely a new image comes from a previous location or a new location.
Real-Time 3D Profiling with RGB-D Mapping in Pipelines Real-Time 3D Profiling with RGB-D Mapping in Pipelines
Real-Time Multi-SLAM System for Agent Localization Real-Time Multi-SLAM System for Agent Localization and 3D Mapping in Dynamic Scenarios Pierre Alliez1, Fabien Bonardi 2, Samia Bouchafa , Jean-Yves Didier , Hicham Hadj-Abdelkader2, Fernando Ireta Munoz 1, Viachaslau Kachurka2, Bastien Rault4, Maxime Robin4, David Roussel2 AbstractThis paper introduces a Wearable SLAM system
Existing 3D reconstruction pipelines mainly differ in the details how tracking , [ 23 ], [ 24 ] and mapping [10 ], [ 11 ], [ 25 ] are implemented. 1.1 Camera Relocalization in Real-Time SLAM In order to acquire an accurate map of the scene, reconstruction pipelines rely on a steady stream of successfully tracked frames. Tracking failure can have
Real-Time RGB-D Camera Relocalization3D reconstruction pipeline exist which mainly differ in the details how tracking [12, 23, 24] and mapping [8, 20, 25] are implemented. e-mail:[email protected] 1.1 Importance of Relocalization In order to acquire an accurate map of the scene, these recon-struction pipelines rely on a
Real-time 3D reconstruction at scale using voxel hashing Online 3D reconstruction is gaining newfound interest due to the availability of real-time consumer depth cameras. The basic problem takes live overlapping depth maps as input and incrementally fuses these into a single 3D model. This is challenging particularly when real-time performance is desired without trading quality or scale.
The real-world scene is captured through multi-camera, or single RGB-D camera, or monocular camera.The general non-rigid 3D reconstruction pipeline is as shown in Fig. 4 Based on the scanning devices acquired data format may vary, either the data is obtained as a point cloud, or the data may be represented as the simple RGB information in the
Real-time Non-rigid Reconstruction using an RGB-D RGB-D Estimation Fused 3D model RGB-Infrared depth sensor Multi-resolution template hierarchy Online Template Acquisition (~1 min) Real-time Non-rigid Reconstruction (30Hz) RGB-D Estimation Figure 2:Main system pipeline. Left:the initial template acquisition is an online process. Multiple views are volumetrically fused, and a
Real-time RGB-D Mapping and 3-D Modeling on the Real-time RGB-D Mapping and 3-D Modeling on the GPU using the Random Ball Cover Data Structure Dominik Neumann1, Felix Lugauer1, Sebastian Bauer1, Jakob Wasza1, Joachim Hornegger1,2 1Pattern Recognition Lab, Department of Computer Science 2Erlangen Graduate School in Advanced Optical Technologies (SAOT) Friedrich-Alexander-Universitat Erlangen-N¨ urnberg, Germany¨
in real-time by the Kintinuous system containing over 2.6 million triangles and 1.5 million colored vertices. and extended scale paths. We also present a novel GPU-implementation of the RGB-D-based visual odometry system of Steinbruecker et al. , enabling real-time execution of the algorithm. Additionally we present a method for Real-Time 3D Profiling with RGB-D Mapping in Pipelines Jul 29, 2019 · Real-Time 3D Profiling with RGB-D Mapping in Pipelines Using Stereo Camera Vision and Structured IR Laser Ring. This paper is focused on delivering a solution that can scan and reconstruct the 3D profile of a pipeline in real-time using a crawler robot. A structured infrared (IR) laser ring projector and a stereo camera system are used to generate the 3D profile of the pipe as the robot