Planar Shape Based Registration for Multi-modal Geometry

International audienceWe present a global registration algorithm for multi-modal geometric data, typically 3D point clouds and meshes. Existing feature-based methods and recent deep learning based approaches typically rely upon point-to-point matching strategies that often fail to deliver accurate results from defect-laden data. In contrast, we reason at the scale of planar shapes whose detection from input data offers robustness on a range of defects, from noise to outliers through heterogeneous sampling. The detected planar shapes are projected into an accumulation space from which a rotational alignment is operated. A second step then refines the result with a local continuous optimization which also estimates the scale. We demonstrate the robustness and efficacy of our algorithm on challenging real-world data. In particular, we show that our algorithm competes well against state-of-the-art methods, especially on piece-wise planar objects and scenes

Effective 3D Geometric Matching for Data Restoration and Its Forensic Application

3D geometric matching is the technique to detect the similar patterns among multiple objects. It is an important and fundamental problem and can facilitate many tasks in computer graphics and vision, including shape comparison and retrieval, data fusion, scene understanding and object recognition, and data restoration. For example, 3D scans of an object from different angles are matched and stitched together to form the complete geometry. In medical image analysis, the motion of deforming organs is modeled and predicted by matching a series of CT images. This problem is challenging and remains unsolved, especially when the similar patterns are 1) small and lack geometric saliency; 2) incomplete due to the occlusion of the scanning and damage of the data. We study the reliable matching algorithm that can tackle the above difficulties and its application in data restoration. Data restoration is the problem to restore the fragmented or damaged model to its original complete state. It is a new area and has direct applications in many scientific fields such as Forensics and Archeology. In this dissertation, we study novel effective geometric matching algorithms, including curve matching, surface matching, pairwise matching, multi-piece matching and template matching. We demonstrate its applications in an integrated digital pipeline of skull reassembly, skull completion, and facial reconstruction, which is developed to facilitate the state-of-the-art forensic skull/facial reconstruction processing pipeline in law enforcement

Automatic tolerance inspection through Reverse Engineering: a segmentation technique for plastic injection moulded parts

This work studies segmentations procedures to recognise features in a Reverse Engineering (RE) application that is oriented to computer-aided tolerance inspection of injection moulding die set-up, necessary to manufacture electromechanical components. It will discuss all steps of the procedures, from the initial acquisition to the final measure data management, but specific original developments will be focused on the RE post-processing method, that should solve the problem related to the automation of the surface recognition and then of the inspection process. As it will be explained in the first two Chapters, automation of the inspection process pertains, eminently, to feature recognition after the segmentation process. This work presents a voxel-based approach with the aim of reducing the computation efforts related to tessellation and curvature analysis, with or without filtering. In fact, a voxel structure approximates the shape through parallelepipeds that include small sub-set of points. In this sense, it represents a filter, since the number of voxels is less than the total number of points, but also a local approximation of the surface, if proper fitting models are applied. Through sensitivity analysis and industrial applications, limits and perspectives of the proposed algorithms are discussed and validated in terms of accuracy and save of time. Validation case-studies are taken from real applications made in ABB Sace S.p.A., that promoted this research. Plastic injection moulding of electromechanical components has a time-consuming die set-up. It is due to the necessity of providing dies with many cavities, which during the cooling phase may present different stamping conditions, thus defects that include lengths outside their dimensional tolerance, and geometrical errors. To increase the industrial efficiency, the automation of the inspection is not only due to the automatic recognition of features but also to a computer-aided inspection protocol (path planning and inspection data management). For this reason, also these steps will be faced, as the natural framework of the thesis research activity. The work structure concerns with six chapters. In Chapter 1, an introduction to the whole procedure is presented, focusing on reasons and utilities of the application of RE techniques in industrial engineering. Chapter 2 analyses acquisition issues and methods that are related to our application, describing: (a) selected hardware; (b) adopted strategy related to the cloud of point acquisition. In Chapter 3, the proposed RE post-processing is described together with a state of art about data segmentation and surface reconstruction. Chapter 4 discusses the proposed algorithms through sensitivity studies concerning thresholds and parameters utilised in segmentation phase and surface reconstruction. Chapter 5 explains briefly the inspection workflow, PDM requirements and solution, together with a preliminary assessing of measures and their reliability. These three chapters (3, 4 and 5) report final sections, called “Discussion”, in which specific considerations are given. Finally, Chapter 6 gives examples of the proposed segmentation technique in the framework of the industrial applications, through specific case studies

Computational processing and analysis of ear images

Tese de mestrado. Engenharia Biomédica. Faculdade de Engenharia. Universidade do Porto. 201

Representation Learning for Shape Decomposition, By Shape Decomposition

The ability to parse 3D objects into their constituent parts is essential for humans to understand and interact with the surrounding world. Imparting this skill in machines is important for various computer graphics, computer vision, and robotics tasks. Machines endowed with this skill can better interact with its surroundings, perform shape editing, texturing, recomposing, tracking, and animation. In this thesis, we ask two questions. First, how can machines decompose 3D shapes into their fundamental parts? Second, does the ability to decompose the 3D shape into these parts help learn useful 3D shape representations? In this thesis, we focus on parsing the shape into compact representations, such as parametric surface patches and Constructive Solid Geometry (CSG) primitives, which are also widely used representations in 3D modeling in computer graphics. Inspired by the advances in neural networks for 3D shape processing, we develop neural network approaches to tackle shape decomposition. First, we present CSGNet, a network architecture to parse shapes into CSG programs, which is trained using combination of supervised and reinforcement learning. Second, we present ParSeNet, a network architecture to decompose a shape into parametric surface patches (B-Spline) and geometric primitives (plane, cone, cylinder and sphere), trained on a large set of CAD models using supervised learning. The training of deep neural network architectures for 3D recognition and generation tasks requires a large amount of labeled datasets. We explore ways to alleviate this problem by relying on shape decomposition methods to guide the learning process. Towards that end, we first study the use of freely available metadata, albeit inconsistent, from shape repositories to learn 3D shape features. Later we show that learning to decompose a 3D shape into geometric primitives also helps in learning shape representations useful for semantic segmentation tasks. Finally, since most 3D shapes encountered in real life are textured, consisting of several fine-grained semantic parts, we propose a method to learn fine-grained representations for textured 3D shapes in a self-supervised manner by incorporating 3D geometric priors

Change of the "Guard": Charlie Rouse, Steve Lacy, and the Music of Thelonious Monk

The word “Monkian” is frequently used in jazz discourse to describe the music of pianist Thelonious Monk. This study consolidates literature on Monk’s music to define the Monkian aesthetic as an integration of the following musical elements: unorthodox jazz harmony, rhythmic displacement, principles of economy, an emphasis on thematic repetition, and technical experimentation. These elements appear in his compositions, which jazz musicians find difficult to perform. The Monkian aesthetic may be apparent in music by other jazz performers who integrate these elements during improvisation. An analysis of selected improvisations by Charlie Rouse and Steve Lacy, two saxophonists who performed Monk’s music extensively, demonstrates this aesthetic. Analyses are conducted on two solos by Rouse in the post-bop style—“Evidence” (1960) and “Rhythm-A-Ning” (1964)—and three recordings by Lacy in the free jazz style: two versions of “Evidence” (1961 and 1985) and “Pannonica” (1963). The Monkian aesthetic is prominent in their music, and is demonstrated through narrative description with the aid of formulaic, schematic, and reduction analysis techniques. Group interaction is shown to play a significant role in their interpretations. I argue that Monk, Rouse, and Lacy were avant-garde jazz musicians. They represent a change in the notion of “avant-garde” in jazz according to the musical analyses and a critical evaluation of their social environment. Monk’s performances, recordings, and public image were avant-garde for the 1940s and 1950s. Rouse followed Monk’s musical conception closely, and by extension, is considered an avant-gardist in jazz. Lacy’s music and his community of musicians helped define the 1960s avant-garde movement in jazz. Both saxophonists contributed to Monk’s legacy in these conceptions of avant-gardism