Study of Subjective and Objective Quality Evaluation of 3D Point Cloud Data by the JPEG Committee

The SC29/WG1 (JPEG) Committee within ISO/IEC is currently working on developing standards for the storage, compression and transmission of 3D point cloud information. To support the creation of these standards, the committee has created a database of 3D point clouds representing various quality levels and use-cases and examined a range of 2D and 3D objective quality measures. The examined quality measures are correlated with subjective judgments for a number of compression levels. In this paper we describe the database created, tests performed and key observations on the problems of 3D point cloud quality assessment

Rate-Distortion Efficient Piecewise Planar 3D Scene Representation from 2-D Images

Cataloged from PDF version of article.In any practical application of the 2-D-to-3-D conversion that involves storage and transmission, representation effi- ciency has an undisputable importance that is not reflected in the attention the topic received. In order to address this problem, a novel algorithm, which yields efficient 3-D representations in the rate distortion sense, is proposed. The algorithm utilizes two views of a scene to build a mesh-based representation incrementally, via adding new vertices, while minimizing a distortion measure. The experimental results indicate that, in scenes that can be approximated by planes, the proposed algorithm is superior to the dense depth map and, in some practical situations, to the block motion vector-based representations in the rate-distortion sense

An intelligent real time 3D vision system for robotic welding tasks

MARWIN is a top-level robot control system that has been designed for automatic robot welding tasks. It extracts welding parameters and calculates robot trajectories directly from CAD models which are then verified by real-time 3D scanning and registration. MARWIN's 3D computer vision provides a user-centred robot environment in which a task is specified by the user by simply confirming and/or adjusting suggested parameters and welding sequences. The focus of this paper is on describing a mathematical formulation for fast 3D reconstruction using structured light together with the mechanical design and testing of the 3D vision system and show how such technologies can be exploited in robot welding tasks

Mobile graphics: SIGGRAPH Asia 2017 course

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Pattern sequences for fast absolute phase retrieval with application in the handheld operation of structured light sensors

Areal 3D scanners based on structured light projection are widely used for inspection tasks in manufacturing processes. For accurate measurements of object geometries, common structured light sensors project a sequence of periodic patterns onto the surface. By subsequent evaluation of these patterns, the local phase of the patterns is reconstructed in a phase retrieval step. The local phase encodes the geometry information and allows for the reconstruction of 3D point clouds in combination with a calibration of the sensor. However, motion of an object during the projection of the pattern sequence leads to large deviations in the reconstructed local phase and thus the 3D point cloud. For dynamic measurements of moving objects, several single-shot techniques have been proposed. While these techniques only require a single pattern for 3D measurements, phase reconstruction suffers when measuring non-diffuse reflective surfaces. In order to evaluate the feasibility of different approaches, sequences of periodic patterns with different lengths are compared based on static and dynamic measurements of a contour standard with a non-diffuse surface using a fiber-endoscopic structured light sensor. Based on the evaluations, pattern sequences are parametrized for dynamic measurements using the flexible sensor head of the endoscopic system. A proof of concept of simultaneous localization and mapping for the handheld operation of the endoscopic sensor head is demonstrated, which can be used for the navigation in narrow spaces in the future

Joint geometry and color point cloud denoising based on graph wavelets

A point cloud is an effective 3D geometrical presentation of data paired with different attributes such as transparency, normal and color of each point. The imperfect acquisition process of a 3D point cloud usually generates a significant amount of noise. Hence, point cloud denoising has received a lot of attention. Most of the existing techniques perform point cloud denoising based only on the geometry information of the neighbouring points; there are very few works considering the problem of denoising of color attributes of a point cloud, and taking advantage of the correlation between geometry and color. In this article, we introduce a novel non-iterative set-up for the denoising of point cloud based on spectral graph wavelet transform (SGW) that jointly exploits geometry and color to perform denoising of geometry and color attributes in graph spectral domain. The designed framework is based on the construction of joint geometry and color graph that compacts the energy of smooth graph signals in the low-frequency bands. The noise is then removed from the spectral graph wavelet coefficients by applying data-driven adaptive soft-thresholding. Extensive simulation results show that the proposed denoising technique significantly outperforms state-of-the-art methods using both subjective and objective quality metrics