Extracting curve-skeletons from digital shapes using occluding contours

Curve-skeletons are compact and semantically relevant shape descriptors, able to summarize both topology and pose of a wide range of digital objects. Most of the state-of-the-art algorithms for their computation rely on the type of geometric primitives used and sampling frequency. In this paper we introduce a formally sound and intuitive definition of curve-skeleton, then we propose a novel method for skeleton extraction that rely on the visual appearance of the shapes. To achieve this result we inspect the properties of occluding contours, showing how information about the symmetry axes of a 3D shape can be inferred by a small set of its planar projections. The proposed method is fast, insensitive to noise, capable of working with different shape representations, resolution insensitive and easy to implement

A model-based approach to recovering the structure of a plant from images

We present a method for recovering the structure of a plant directly from a small set of widely-spaced images. Structure recovery is more complex than shape estimation, but the resulting structure estimate is more closely related to phenotype than is a 3D geometric model. The method we propose is applicable to a wide variety of plants, but is demonstrated on wheat. Wheat is made up of thin elements with few identifiable features, making it difficult to analyse using standard feature matching techniques. Our method instead analyses the structure of plants using only their silhouettes. We employ a generate-and-test method, using a database of manually modelled leaves and a model for their composition to synthesise plausible plant structures which are evaluated against the images. The method is capable of efficiently recovering accurate estimates of plant structure in a wide variety of imaging scenarios, with no manual intervention

Reconstructing Plants in 3D from a Single Image Using Analysis-by-Synthesis

International audienceMature computer vision techniques allow the reconstruction of challenging 3D objects from images. However, due to high complexity of plant topology, dedicated methods for generating 3D plant models must be devised. We propose to generate a 3D model of a plant, using an analysis-by-synthesis method mixing information from a single image and a priori knowledge of the plant species. First, our dedicated skeletonisation algorithm generates a possible branch- ing structure from the foliage segmentation. Then, a 3D generative model, based on a parametric model of branching systems that takes into ac- count botanical knowledge is built. The resulting skeleton follows the hierarchical organisation of natural branching structures. An instance of a 3D model can be generated. Moreover, varying parameter values of the generative model (main branching structure of the plant and foliage), we produce a series of candidate models. The reconstruction is improved by selecting the model among these proposals based on a matching criterion with the image. Realistic results obtained on di erent species of plants illustrate the performance of the proposed method

Medial Skeletal Diagram: A Generalized Medial Axis Approach for Compact 3D Shape Representation

We propose the Medial Skeletal Diagram, a novel skeletal representation that tackles the prevailing issues around compactness and reconstruction accuracy in existing skeletal representations. Our approach augments the continuous elements in the medial axis representation to effectively shift the complexity away from discrete elements. To that end, we introduce generalized enveloping primitives, an enhancement of the standard primitives in medial axis, which ensures efficient coverage of intricate local features of the input shape and substantially reduces the number of discrete elements required. Moreover, we present a computational framework that constructs a medial skeletal diagram from an arbitrary closed manifold mesh. Our optimization pipeline ensures that the resulting medial skeletal diagram comprehensively covers the input shape with the fewest primitives. Additionally, each optimized primitive undergoes a post-refinement process to guarantee an accurate match with the source mesh in both geometry and tessellation. We validate our approach on a comprehensive benchmark of 100 shapes, demonstrating its compactness of the discrete elements and superior reconstruction accuracy across a variety of cases. Furthermore, we exemplify the versatility of our representation in downstream applications such as shape optimization, shape generation, mesh decomposition, mesh alignment, mesh compression, and user-interactive design.Comment: 22 pages, 28 figure

ПОСТРОЕНИЕ КРИВОЛИНЕЙНОГО СКЕЛЕТА ТРЕХМЕРНОЙ МОДЕЛИ ПО ПЛОСКИМ ПРОЕКЦИЯМ

В данной работе предложен новый алгоритм построения криволинейного скелета для широкого класса объектов. Алгоритм использует аппроксимацию объекта его визуальной оболочкой, что дает нам возможность работать с моделью, используя только ее силуэты. Предлагается эффективный алгоритм для вычисления 3D карты расстояний для внутренних вокселей визуальной оболочки. Используя эту 3D карту расстояний, организуется обратное проецирование непрерывных скелетов плоских проекций, формирующих визуальную оболочку. Полученное облако точек является первой аппроксимацией криволинейного скелета. Затем используется набор техник фильтрации и кластеризации полученного облака с целью получения менее шумной аппроксимации. Полученная аппроксимация уже может использоваться для приложений. Далее организуется итерационный процесс для уточнения криволинейного скелета. Описываемый метод показал существенное улучшение времени вычисления по сравнению с существующими методами. Метод показал хорошие результаты построения криволинейного скелета для моделей со сложной геометрией и топологией. Получаемые криволинейные скелеты удовлетворяют большинству требований, предъявляемым к универсальным криволинейным скелета

Spatial Reconstruction of Biological Trees from Point Cloud

Trees are complex systems in nature whose topology and geometry ar