Point Cloud Manager

Point clouds provide a flexible geometric representation suitable for countless applications in computer graphics; they also comprise the raw output of most 3D data acquisition devices. While hand-designed features on point clouds have long been proposed in graphics and vision, however, the recent overwhelming success of convolutional neural networks (CNNs) for image analysis suggests the value of adapting insight from CNN to the point cloud world. Point clouds inherently lack topological information, so designing a model to recover topology can enrich the representation power of point clouds. To this end, we propose a new neural network module dubbed EdgeConv suitable for CNN-based high-level tasks on point clouds, including classification and segmentation. EdgeConv acts on graphs dynamically computed in each layer of the network. It is differentiable and can be plugged into existing architectures. Compared to existing modules operating in extrinsic space or treating each point independently, EdgeConv has several appealing properties: It incorporates local neighborhood information; it can be stacked applied to learn global shape properties; and in multi-layer systems affinity in feature space captures semantic characteristics over potentially long distances in the original embedding. We show the performance of our model on standard benchmarks, including ModelNet40, ShapeNetPart, and S3DIS.

The purpose of this volume is to present current work of the Intelligent Computer Graphics community, a community growing up year after year. Indeed, if at the beginning of Computer Graphics the use of Artificial Intelligence techniques was quite unknown, more and more researchers all over the world are nowadays interested in intelligent techniques allowing substantial improvements of traditional Computer Graphics methods. The other main contribution of intelligent techniques in Computer Graphics is to allow invention of completely new methods, often based on automation of a lot of tasks assumed in the past by the user in an imprecise and (human) time consuming manner. This volume contains both invited and selected extended papers from the last 3IA Conference (3IA2008), together with an introduction presenting the area of Intelligent Computer Graphics and various Computer Graphics areas where introduction of intelligent techniques permitted to resolve important problems. We hope that this volume will be interesting for the reader and that it will convince him (her) to use, or to invent, intelligent techniques in Computer Graphics and, maybe, to join the Intelligent Computer Graphics community.

Modern operating rooms are highly complex, safety-critical environments in which surgeons must maintain sustained concentration and an uninterrupted workflow to ensure optimal patient outcomes. Despite significant technological advances, several fundamental challenges remain insufficiently addressed. One such challenge is achieving optimal and consistent illumination of the surgical field. In current operating rooms, surgical lights must often be manually repositioned to compensate for shadows cast by medical personnel, leading to frequent workflow interruptions and increased cognitive load. Another persistent challenge is providing surgeons with immediate and non-distracting access to critical information. As the amount of information required during surgery continues to grow, presenting this information on remote monitors can disrupt workflow and divert attention away from the surgical field. Both interruptions can compromise safety and efficiency. In this dissertation, I address both challenges by developing algorithms based on multi-view depth camera technology that enable shadow-free illumination of the surgical field and shadow-free projection of information adjacent to it. For shadow-free illumination, I propose algorithms for an autonomous surgical lighting system that detect occlusions caused by medical personnel and dynamically control ceiling-mounted light modules to ensure optimal illumination. For shadow-free projection, I develop algorithms for a multi-projector system that displays information directly next to the surgical site. While multiple projectors are employed to facilitate shadow-free projection, my algorithms specifically enable precise geometric distortion correction on dynamic, uneven surfaces. In addition, I present a blur-mitigation strategy that significantly reduces ghosting artifacts caused by overlapping projections and unavoidable minor errors in dynamic geometry reconstruction. Beyond the medical application itself, I developed novel, general-purpose multi-view point cloud algorithms that enable these systems: (a) a fast, accurate, and robust multi-depth-sensor registration method that allows rapid registration between surgeries while complying with data privacy constraints; (b) BlendPCR, a real-time multi-camera point cloud rendering technique that preserves the native sensor resolution while enabling seamless visualization and precise geometric correction for projection onto dynamic, deformable surfaces; and (c) TemPCC, an experimental approach for temporal point cloud completion. I demonstrate that the autonomous surgical lighting system significantly improves illumination quality and shadow reduction in a simulated surgical environment, compared to conventional surgical lights, based on validation with practicing surgeons (n = 11). Furthermore, the multi-projector system enables shadow-free projection onto dynamic, deformable surfaces with a visual accuracy of 1–2 mm using point cloud–based rendering, while the proposed blur mitigation pipeline significantly reduces blurring and ghosting (n = 23). Beyond the surgical domain, my proposed multi-depth-sensor registration method attains an accuracy of 1.6 mm at a distance of 2 m without requiring RGB or infrared data, and could therefore be adapted to LiDAR-based setups. My multi-point cloud rendering technique, BlendPCR, enables low-latency, real-time surface reconstruction and rendering. It achieves frame rates exceeding 200 fps at ultra-high-definition resolution while processing data from seven RGB-D sensors simultaneously, making it suitable for live virtual reality and telepresence scenarios. In summary, my dissertation establishes a technical and algorithmic foundation for reducing distractions in the operating room while also contributing general-purpose methods for multi-view point cloud registration, rendering, and completion, providing a basis for future research and applications in computer graphics and computer vision.

Recently, terrestrial laser scanners have been significantly improved in terms of accuracy, measurement distance, measurement speed, and resolution. They enable us to capture dense 3D point clouds of large-scale objects and fields, such as factories, engineering plants, large equipment, and transport ships. In addition, the mobile mapping system, which is a vehicle equipped with laser scanners and GPSs, can be used for capturing large-scale point clouds from a wide range of roads, buildings, and roadside objects. Large-scale point clouds are useful in a variety of applications, such as renovation and maintenance of facilities, engineering simulation, asset management, and 3D mapping. To realize these applications, new techniques must be developed for processing large-scale point clouds. So far, point processing has been studied mainly for relatively small objects in the field of computer-aided design and computer graphics. However, in recent years, the application areas of point clouds are not limited to conventional domains, but also include manufacturing, civil engineering, construction, transportation, forestry, and so on. This is because the state-of-the-art laser scanner can be used to represent large objects or fields as dense point clouds. We believe that discussing new techniques and applications related to large-scale point clouds beyond the boundaries of traditional academic fields is very important.This special issue addresses the latest research advances in large-scale point cloud processing. This covers a wide area of point processing, including shape reconstruction, geometry processing, object recognition, registration, visualization, and applications. The papers will help readers explore and share their knowledge and experience in technologies and development techniques.All papers were refereed through careful peer reviews. We would like to express our sincere appreciation to the authors for their submissions and to the reviewers for their invaluable efforts for ensuring the success of this special issue.

In reverse engineering, the point cloud data processing is the key, it will directly affect the quality and efficiency of the subsequent reconstruction of the surface. However, the amount of data obtained by three-dimensional laser scanning is often larger and redundant, which will increase the difficulty of the surface reconstruction, therefore, the point cloud data is needed to streamline. Depth analysis of the efficiency and accuracy of curvature grid method, a secondary streamlined algorithm is proposed, which take use of non-uniform grid streamline law based on octree to streamline to the point cloud, and re-use curvature streamline method based on B-spline surface fitting to streamline it. This method, whose streamline ratio is relatively high, can effectively retain the feature point cloud data, providing a great convenience for the follow-up of reverse engineering.

Surface reconstruction from point clouds has been a well-studied research topic with applications in computer vision and computer graphics. Recently, several learningbased methods were proposed for 3D shape representation through implicit functions which among others can be used for point cloud-based reconstruction. Although delivering compelling results for synthetic object datasets of overseeable size, they fail to represent larger scenes accurately, presumably due to the use of only one global latent code for encoding an entire scene or object. We propose to encode only parts of objects with features attached to unstructured point clouds. To this end we use a hierarchical feature map in 3D space, extracted from the input point clouds, with which local latent shape encodings can be queried at arbitrary positions. We use a permutohedral lattice to process the hierarchical feature maps sparsely and efficiently. This enables accurate and detailed point cloud-based reconstructions for large amounts of points in a time-efficient manner, showing good generalization capabilities across different datasets. Experiments on synthetic and real world datasets demonstrate the reconstruction capability of our method and compare favorably to state-of-the-art methods.

Computer graphics tools and techniques enable researchers to investigate cultural heritage and archaeological sites. They can facilitate documentation of real-world sites for further investigation, and enable archaeologists and historians to accurately study a past environment through simulations. This chapter explores how light plays a major role in examining computer-based representations of heritage. We discuss how light is both documented and modelled today using computer graphics techniques and tools. We also identify why both physical and historical accuracy in modelling light is becoming increasingly important to study the past, and how emerging technologies such as High Dynamic Range (HDR) imaging and physically-based rendering is necessary to accurately represent heritage.

This paper presents a survey of ocean simulation and rendering methods in computer graphics. To model and animate the ocean’s surface, these methods mainly rely on two main approaches: on the one hand, those which approximate ocean dynamics with parametric, spectral or hybrid models and use empirical laws from oceanographic research. We will see that this type of methods essentially allows the simulation of ocean scenes in the deep water domain, without breaking waves. On the other hand, physically‐based methods use Navier–Stokes equations to represent breaking waves and more generally ocean surface near the shore. We also describe ocean rendering methods in computer graphics, with a special interest in the simulation of phenomena such as foam and spray, and light’s interaction with the ocean surface.

A hybrid method for skeleton extraction on Kinect sensor data: Combination of L1-Median and Laplacian shrinking algorithms

The computer graphics that revolutionized movies and video games are moving into medicine. Surgeons are now using computers for preoperative planning, “operating” on three-dimensional models via high-performance computer graphics and gaining valuable data to guide their medical and surgical decisions. Researchers use computer graphics to design molecular models of drugs. In the future, physicians may be able to use computer simulations to practice endoscopy (for example, simulating the movement of an endoscope through a colon).

Point cloud up-sampling network with multi-level spatial local feature aggregation

Rigid registration of partial observations is a fundamental problem in various applied fields. In computer graphics, special attention has been given to the registration between two partial point clouds generated by scanning devices. State-of-the-art registration techniques still struggle when the overlap region between the two point clouds is small, and completely fail if there is no overlap between the scan pairs. In this article, we present a learning-based technique that alleviates this problem, and allows registration between point clouds, presented in arbitrary poses, and having little or even no overlap, a setting that has been referred to as tele-registration. Our technique is based on a novel neural network design that learns a prior of a class of shapes and can complete a partial shape. The key idea is combining the registration and completion tasks in a way that reinforces each other. In particular, we simultaneously train the registration network and completion network using two coupled flows, one that register-and-complete, and one that complete-and-register, and encourage the two flows to produce a consistent result. We show that, compared with each separate flow, this two-flow training leads to robust and reliable tele-registration, and hence to a better point cloud prediction that completes the registered scans. It is also worth mentioning that each of the components in our neural network outperforms state-of-the-art methods in both completion and registration. We further analyze our network with several ablation studies and demonstrate its performance on a large number of partial point clouds, both synthetic and real-world, that have only small or no overlap.

Computer graphics applications, software, and hardware research, are examined and evaluated in Continental Western Europe. Growth has been slow. The reasons are the high cost of hardware and the complexity of the required software. The past reveals a rather dim picture. Mechanical engineering applications have shown some promising success. Electrical engineering applications have been effective mainly for PCB and IC mask layout only. Interactive graphic software has not yet effectively found its way outside Fortran. APL-G seems an interesting graphic extension of the language APL. Promising trends appear to be on the horizon. They are related to hardware and software trade-offs, as, for example, in the TALENT System.The need for computer graphics still exists. The hardware and software tradeoffs will receive more attention. Success in computer graphics will depend upon advances in computer systems design, especially programming languages and systems. Computer graphics has yet to come of age.

ZAMM - Journal of Applied Mathematics and Mechanics / Zeitschrift für Angewandte Mathematik und MechanikVolume 86, Issue 2 p. 150-150 Book Review Book Review: Helmut Ratschek and Jon Rokne, Geometric Computations with Interval and New Robust Methods. Applications in Computer Graphics, GIS and Computational Geometry G. Alefeld, G. Alefeld KarlsruheSearch for more papers by this author G. Alefeld, G. Alefeld KarlsruheSearch for more papers by this author First published: 20 January 2006 https://doi.org/10.1002/zamm.200690009AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat No abstract is available for this article. Volume86, Issue2February 2006Pages 150-150 RelatedInformation

This paper presents a general Surface Reconstruction framework which encapsulates the uncertainty of the sam-pled data, making no assumption on the shape of the surface to be reconstructed. Starting from the input points(either points clouds or multiple range images), an Estimated Existence Function (EEF) is built which modelsthe space in which the desired surface could exist and, by the extraction of EEF critical points, the surface isreconstructed. The nal goal is the development of a generic framework able to adapt the result to different kindsof additional information coming from multiple sensors. Categories and Subject Descriptors (according to ACM CCS) : I.3.3 [Computer Graphics]: Shape Modeling, Uncer-tain data, Multi-sensor Data Fusion 1. Introduction 3D scanning devices are becoming more and more availableand affordable. Thanks to modern acquisition technologies,heterogeneous data can be acquired from multiple acquisi-tion sensors, which often incorporate information about un-certainty of the data sampling process. Surface reconstruc-tion techniques designed over a specic sensor often takeinto account uncertainty during the reconstruction process,but they are limited to work with a single device. On thecontrary, general techniques that can process data comingfrom different sensors usually disregard much part of sensor-specic information, and seldom take into account uncer-tainty.The basic concept of our approach is