No Clamp Robotic Assembly with Use of Point Cloud Data from Low-Cost Triangulation Scanner

The paper shows the clamp-less assembly idea as a very important one in the modern assembly. Assembly equipment such as clamps represent a significant group of industrial equipment in manufacturing plants whose number can be effectively reduced. The article presents the concept of using industrial robot equipped with a triangulation scanner in the assembly process in order to minimize the number of clamps that hold the units in a particular position in space. It also shows how the system searches for objects in the point cloud based on multi-step processing algorithm proposed in this work, then picks them up, transports and positions them in the right assembly locations with the use of industrial robot manipulator. The accuracy of the positioning of parts was also examined as well as the impact of the number of iterations of the algorithm searching the models in the point cloud on the accuracy of determining the position of the objects. The tests show that presented system is suitable for assembly of various items as plastic packaging and palletizing of products. Such kind of system is the basis for modern, fully flexible assembly systems

Generic implementation of CAD models for nuclear simulation

The goal of this project is to utilize the preexisting framework of GADRAS to simulate the radiation leakage from arbitrary CAD models without sacrificing speed or accuracy. The proposed solution is to use STL files to define models. Then, a three-dimension binning structure is created to contain all the elements of the file. This results in preservation of speed, without adding higher performance hardware requirements. Finally, the discretization is performed using a three-dimension framework to utilize GADRAS’ refinement algorithm. The combination of these two enhancements results in an absolute error within 10% for standard conditions, and 20% for edge case conditions. The addition of arbitrary models will simplify the modeling process for complex shapes, allow for more flexible models, and allow for creation of models that are simply impossible in the current framework.Mechanical Engineerin

Unstructured surface and volume decimation of tessellated domains

A general algorithm for decimating unstructured discretized data sets is presented. The discretized space may be a planar triangulation, a general 3D surface triangulation, or a 3D tetrahedrization. The decimation algorithm enforces Dirichlet boundary conditions, uses only existing vertices, and assumes manifold geometry. Local dynamic vertex removal is performed without history information while preserving the initial topology and boundary geometry. The tessellation at each step of the algorithm is preserved and, in the pathological case, every interior vertex is a candidate for removal. The research focuses on how to remove a vertex from an existing unstructured n-dimensional tessellation, not on the formulation of decimation criteria. Criteria for removing a candidate vertex may be based on geometric properties or any scalar governing function specific to the application. Use of scalar functions to adaptively control or optimize tessellation resolution is particularly applicable to the computer graphics, computational fluids, and structural analysis disciplines. Potential applications in the geologic exploration and medical or industrial imaging fields are promising

The role of object instance re-identification in 3D object localization and semantic 3D reconstruction.

For an autonomous system to completely understand a particular scene, a 3D reconstruction of the world is required which has both the geometric information such as camera pose and semantic information such as the label associated with an object (tree, chair, dog, etc.) mapped within the 3D reconstruction. In this thesis, we will study the problem of an object-centric 3D reconstruction of a scene in contrast with most of the previous work in the literature which focuses on building a 3D point cloud that has only the structure but lacking any semantic information. We will study how crucial 3D object localization is for this problem and will discuss the limitations faced by the previous related methods. We will present an approach for 3D object localization using only 2D detections observed in multiple views by including 3D object shape priors. Since our first approach relies on associating 2D detections in multiple views, we will also study an approach to re-identify multiple object instances of an object in rigid scenes and will propose a novel method of joint learning of the foreground and background of an object instance using a triplet-based network in order to identify multiple instances of the same object in multiple views. We will also propose an Augmented Reality-based application using Google's Tango by integrating both the proposed approaches. Finally, we will conclude with some open problems that might benefit from the suggested future work

Man-made Surface Structures from Triangulated Point Clouds

Photogrammetry aims at reconstructing shape and dimensions of objects captured with cameras, 3D laser scanners or other spatial acquisition systems. While many acquisition techniques deliver triangulated point clouds with millions of vertices within seconds, the interpretation is usually left to the user. Especially when reconstructing man-made objects, one is interested in the underlying surface structure, which is not inherently present in the data. This includes the geometric shape of the object, e.g. cubical or cylindrical, as well as corresponding surface parameters, e.g. width, height and radius. Applications are manifold and range from industrial production control to architectural on-site measurements to large-scale city models. The goal of this thesis is to automatically derive such surface structures from triangulated 3D point clouds of man-made objects. They are defined as a compound of planar or curved geometric primitives. Model knowledge about typical primitives and relations between adjacent pairs of them should affect the reconstruction positively. After formulating a parametrized model for man-made surface structures, we develop a reconstruction framework with three processing steps: During a fast pre-segmentation exploiting local surface properties we divide the given surface mesh into planar regions. Making use of a model selection scheme based on minimizing the description length, this surface segmentation is free of control parameters and automatically yields an optimal number of segments. A subsequent refinement introduces a set of planar or curved geometric primitives and hierarchically merges adjacent regions based on their joint description length. A global classification and constraint parameter estimation combines the data-driven segmentation with high-level model knowledge. Therefore, we represent the surface structure with a graphical model and formulate factors based on likelihood as well as prior knowledge about parameter distributions and class probabilities. We infer the most probable setting of surface and relation classes with belief propagation and estimate an optimal surface parametrization with constraints induced by inter-regional relations. The process is specifically designed to work on noisy data with outliers and a few exceptional freeform regions not describable with geometric primitives. It yields full 3D surface structures with watertightly connected surface primitives of different types. The performance of the proposed framework is experimentally evaluated on various data sets. On small synthetically generated meshes we analyze the accuracy of the estimated surface parameters, the sensitivity w.r.t. various properties of the input data and w.r.t. model assumptions as well as the computational complexity. Additionally we demonstrate the flexibility w.r.t. different acquisition techniques on real data sets. The proposed method turns out to be accurate, reasonably fast and little sensitive to defects in the data or imprecise model assumptions.Künstliche Oberflächenstrukturen aus triangulierten Punktwolken Ein Ziel der Photogrammetrie ist die Rekonstruktion der Form und Größe von Objekten, die mit Kameras, 3D-Laserscannern und anderern räumlichen Erfassungssystemen aufgenommen wurden. Während viele Aufnahmetechniken innerhalb von Sekunden triangulierte Punktwolken mit Millionen von Punkten liefern, ist deren Interpretation gewöhnlicherweise dem Nutzer überlassen. Besonders bei der Rekonstruktion künstlicher Objekte (i.S.v. engl. man-made = „von Menschenhand gemacht“ ist man an der zugrunde liegenden Oberflächenstruktur interessiert, welche nicht inhärent in den Daten enthalten ist. Diese umfasst die geometrische Form des Objekts, z.B. quaderförmig oder zylindrisch, als auch die zugehörigen Oberflächenparameter, z.B. Breite, Höhe oder Radius. Die Anwendungen sind vielfältig und reichen von industriellen Fertigungskontrollen über architektonische Raumaufmaße bis hin zu großmaßstäbigen Stadtmodellen. Das Ziel dieser Arbeit ist es, solche Oberflächenstrukturen automatisch aus triangulierten Punktwolken von künstlichen Objekten abzuleiten. Sie sind definiert als ein Verbund ebener und gekrümmter geometrischer Primitive. Modellwissen über typische Primitive und Relationen zwischen Paaren von ihnen soll die Rekonstruktion positiv beeinflussen. Nachdem wir ein parametrisiertes Modell für künstliche Oberflächenstrukturen formuliert haben, entwickeln wir ein Rekonstruktionsverfahren mit drei Verarbeitungsschritten: Im Rahmen einer schnellen Vorsegmentierung, die lokale Oberflächeneigenschaften berücksichtigt, teilen wir die gegebene vermaschte Oberfläche in ebene Regionen. Unter Verwendung eines Schemas zur Modellauswahl, das auf der Minimierung der Beschreibungslänge beruht, ist diese Oberflächensegmentierung unabhängig von Kontrollparametern und liefert automatisch eine optimale Anzahl an Regionen. Eine anschließende Verbesserung führt eine Menge von ebenen und gekrümmten geometrischen Primitiven ein und fusioniert benachbarte Regionen hierarchisch basierend auf ihrer gemeinsamen Beschreibungslänge. Eine globale Klassifikation und bedingte Parameterschätzung verbindet die datengetriebene Segmentierung mit hochrangigem Modellwissen. Dazu stellen wir die Oberflächenstruktur in Form eines graphischen Modells dar und formulieren Faktoren basierend auf der Likelihood sowie auf apriori Wissen über die Parameterverteilungen und Klassenwahrscheinlichkeiten. Wir leiten die wahrscheinlichste Konfiguration von Flächen- und Relationsklassen mit Hilfe von Belief-Propagation ab und schätzen eine optimale Oberflächenparametrisierung mit Bedingungen, die durch die Relationen zwischen benachbarten Primitiven induziert werden. Der Prozess ist eigens für verrauschte Daten mit Ausreißern und wenigen Ausnahmeregionen konzipiert, die nicht durch geometrische Primitive beschreibbar sind. Er liefert wasserdichte 3D-Oberflächenstrukturen mit Oberflächenprimitiven verschiedener Art. Die Leistungsfähigkeit des vorgestellten Verfahrens wird an verschiedenen Datensätzen experimentell evaluiert. Auf kleinen, synthetisch generierten Oberflächen untersuchen wir die Genauigkeit der geschätzten Oberflächenparameter, die Sensitivität bzgl. verschiedener Eigenschaften der Eingangsdaten und bzgl. Modellannahmen sowie die Rechenkomplexität. Außerdem demonstrieren wir die Flexibilität bzgl. verschiedener Aufnahmetechniken anhand realer Datensätze. Das vorgestellte Rekonstruktionsverfahren erweist sich als genau, hinreichend schnell und wenig anfällig für Defekte in den Daten oder falsche Modellannahmen

Parallel Contour Path Planning for Complicated Cavity Part Fabrication using Voronoi-based Distance Map

To generate parallel contour path for direct production of complicated cavity component, a novel path planning based on Voronoi-based distance map is presented in this paper. Firstly, the grid representation of polygonal slice is produced by hierarchical rasterization using graphics hardware acceleration and divided into Voronoi cells of contour by an exact EDT (Euclidean distance transformation). Then, each VCI (Voronoi cell of inner contour) is further subdivided into CLRI (closed loop region of inner contour) and OLRI (open loop region of inner contour). Closed paths for each CLRI and the block merging VCO (Voronoi cell of outer contour) and all OLRIs are generated by local and global isoline extraction, respectively. The final path ordered in circumferential and radial directions is obtained by sorting and connecting all individual paths. In comparison with conventional methods such as pair-wise intersection and Voronoi diagram, the proposed algorithm is numerically robust, can avoid null path and self-intersection because of the application of distance map and discrete Voronoi diagram. It is especially used for FGM (Functionally Graded Material) design and fabrication.Mechanical Engineerin

Detector-Free Structure from Motion

We propose a new structure-from-motion framework to recover accurate camera poses and point clouds from unordered images. Traditional SfM systems typically rely on the successful detection of repeatable keypoints across multiple views as the first step, which is difficult for texture-poor scenes, and poor keypoint detection may break down the whole SfM system. We propose a new detector-free SfM framework to draw benefits from the recent success of detector-free matchers to avoid the early determination of keypoints, while solving the multi-view inconsistency issue of detector-free matchers. Specifically, our framework first reconstructs a coarse SfM model from quantized detector-free matches. Then, it refines the model by a novel iterative refinement pipeline, which iterates between an attention-based multi-view matching module to refine feature tracks and a geometry refinement module to improve the reconstruction accuracy. Experiments demonstrate that the proposed framework outperforms existing detector-based SfM systems on common benchmark datasets. We also collect a texture-poor SfM dataset to demonstrate the capability of our framework to reconstruct texture-poor scenes. Based on this framework, we take $\textit{first place}$ in Image Matching Challenge 2023.Comment: Project page: https://zju3dv.github.io/DetectorFreeSfM

Vision and advocacy of optoelectronic technology developments in the AECO sector

Purpose This research presents a literature review of laser scanning and 3D modelling devices, modes of delivery and applications within the architecture, engineering, construction and owner-operated (AECO) sector. Such devices are inextricably linked to modern digital built environment practices, particularly when used in conjunction with as-built building information modelling (BIM) development. The research also reports upon innovative technological advancements (such as machine vision) that coalesce with 3D scanning solutions. Design/methodology/approach A synthesis of literature is used to develop: a hierarchy of the modes of delivery for laser scan devices; a thematic analysis of 3D terrestrial laser scan technology applications; and a componential cross-comparative tabulation of laser scan technology and specifications. Findings Findings reveal that the costly and labour intensive attributes of laser scanning devices have stimulated the development of hybrid automated and intelligent technologies to improve performance. Such developments are set to satisfy the increasing demand for digitisation of both existing and new buildings into BIM. Future work proposed will seek to: review what coalescence of digital technologies will provide an optimal and cost effective solution to accurately reconstructing the digital built environment; conduct case studies that implement hybrid digital solutions in pragmatic facilities management scenarios to measure their performance and user satisfaction; and eliminate manual remodelling tasks (such as point cloud reconstruction) via the use of computational intelligence algorithms integral within cloud based BIM platforms. Originality/value Although laser scanning and 3D modelling have been widely covered en passant within the literature, scant research has conducted an holistic review of the technology, its applications and future developments. This review presents concise and lucid reference guidance that will intellectually challenge, and better inform, both practitioners and researchers

Patch-based Progressive 3D Point Set Upsampling

We present a detail-driven deep neural network for point set upsampling. A high-resolution point set is essential for point-based rendering and surface reconstruction. Inspired by the recent success of neural image super-resolution techniques, we progressively train a cascade of patch-based upsampling networks on different levels of detail end-to-end. We propose a series of architectural design contributions that lead to a substantial performance boost. The effect of each technical contribution is demonstrated in an ablation study. Qualitative and quantitative experiments show that our method significantly outperforms the state-of-the-art learning-based and optimazation-based approaches, both in terms of handling low-resolution inputs and revealing high-fidelity details.Comment: accepted to cvpr2019, code available at https://github.com/yifita/P3