Active Image-based Modeling with a Toy Drone

Image-based modeling techniques can now generate photo-realistic 3D models from images. But it is up to users to provide high quality images with good coverage and view overlap, which makes the data capturing process tedious and time consuming. We seek to automate data capturing for image-based modeling. The core of our system is an iterative linear method to solve the multi-view stereo (MVS) problem quickly and plan the Next-Best-View (NBV) effectively. Our fast MVS algorithm enables online model reconstruction and quality assessment to determine the NBVs on the fly. We test our system with a toy unmanned aerial vehicle (UAV) in simulated, indoor and outdoor experiments. Results show that our system improves the efficiency of data acquisition and ensures the completeness of the final model.Comment: To be published on International Conference on Robotics and Automation 2018, Brisbane, Australia. Project Page: https://huangrui815.github.io/active-image-based-modeling/ The author's personal page: http://www.sfu.ca/~rha55

Reconstruction of tree branching structures from UAV-LiDAR data

The reconstruction of tree branching structures is a longstanding problem in Computer Graphics which has been studied over several data sources, from photogrammetry point clouds to Terrestrial and Aerial Laser Imaging Detection and Ranging technology. However, most data sources present acquisition errors that make the reconstruction more challenging. Among them, the main challenge is the partial or complete occlusion of branch segments, thus leading to disconnected components whether the reconstruction is resolved using graph-based approaches. In this work, we propose a hybrid method based on radius-based search and Minimum Spanning Tree for the tree branching reconstruction by handling occlusion and disconnected branches. Furthermore, we simplify previous work evaluating the similarity between ground-truth and reconstructed skeletons. Using this approach, our method is proved to be more effective than the baseline methods, regarding reconstruction results and response time. Our method yields better results on the complete explored radii interval, though the improvement is especially significant on the Ground Sampling Distance In terms of latency, an outstanding performance is achieved in comparison with the baseline method.Junta de Andalucia 1381202-GEU PYC20-RE-005-UJAEuropean Commission Spanish Government PID2021-126339OB-I00 FPU17/01902 FPU19/0010

Modélisation de vignes à partir d'une séquence d'images

National audienceCet article présente des travaux sur la modélisation de plantes à géométries fortement contraintes à partir d'images. A partir de séquences d'images acquises dans un vignoble, nous instancions un modèle paramétré des parcelles, des rangs, et des pieds de vignes. Le modèle est déduit des connaissances a priori ; à partir des images, des paramètres sont extraits. Ces paramètres sont ensuite fournis au modèle qui génère une représentation de la plante, du rang ou de la parcelle filmée

Modeling and generating moving trees from video

We present a probabilistic approach for the automatic production of tree models with convincing 3D appearance and motion. The only input is a video of a moving tree that provides us an initial dynamic tree model, which is used to generate new individual trees of the same type. Our approach combines global and local constraints to construct a dynamic 3D tree model from a 2D skeleton. Our modeling takes into account factors such as the shape of branches, the overall shape of the tree, and physically plausible motion. Furthermore, we provide a generative model that creates multiple trees in 3D, given a single example model. This means that users no longer have to make each tree individually, or specify rules to make new trees. Results with different species are presented and compared to both reference input data and state of the art alternatives

Three-dimensional reconstruction of plant shoots from multiple images using an active vision system

The reconstruction of 3D models of plant shoots is a challenging problem central to the emerging discipline of plant phenomics – the quantitative measurement of plant structure and function. Current approaches are, however, often limited by the use of static cameras. We propose an automated active phenotyping cell to reconstruct plant shoots from multiple images using a turntable capable of rotating 360 degrees and camera mounted robot arm. To overcome the problem of static camera positions we develop an algorithm capable of analysing the environment and determining viewpoints from which to capture initial images suitable for use by a structure from motion technique

Approaches to three-dimensional reconstruction of plant shoot topology and geometry

There are currently 805 million people classified as chronically undernourished, and yet the World’s population is still increasing. At the same time, global warming is causing more frequent and severe flooding and drought, thus destroying crops and reducing the amount of land available for agriculture. Recent studies show that without crop climate adaption, crop productivity will deteriorate. With access to 3D models of real plants it is possible to acquire detailed morphological and gross developmental data that can be used to study their ecophysiology, leading to an increase in crop yield and stability across hostile and changing environments. Here we review approaches to the reconstruction of 3D models of plant shoots from image data, consider current applications in plant and crop science, and identify remaining challenges. We conclude that although phenotyping is receiving an increasing amount of attention – particularly from computer vision researchers – and numerous vision approaches have been proposed, it still remains a highly interactive process. An automated system capable of producing 3D models of plants would significantly aid phenotyping practice, increasing accuracy and repeatability of measurements

Reconstruction de modèles virtuels de vignes à partir d'images

National audienceWe propose a method for recontructing virtual model of vines from images. For this, an analysis by synthesis method is used and consist in characterizing an image using a number of a priori knowledge about the 3D scene. Initially, we get an approximation of the plant modeled in 3D. Then, comparing its reprojection with the original image, we refine this model though an iterative optimisation process. To be efficient, our method do not optimize positionning of individual leaves but rather a realistic foliage consistent with the images.Nous présentons ici une méthode de reconstruction de modèles virtuels de pieds de vignes à partir d'images. Pour cela, nous utilisons une technique d'analyse par synthèse qui consiste à caractériser une image à partir d'un certain nombre de connaissances a priori sur la scène 3D. Tout d'abord, à partir d'une première analyse des images, nous obtenons une approximation initiale de la plante modélisée en 3D. Puis, nous affinons cette modélisation par un processus d'optimisation où la projection du modèle reconstruit est comparée avec l'image d'origine. Pour être efficace, notre méthode n'optimise pas le positionnement de chacune des feuilles mais un feuillage réaliste et cohérent avec les images

Computing and fabricating multilayer models

We present a method for automatically converting a digital 3D model into a multilayer model: a parallel stack of high-resolution 2D images embedded within a semi-transparent medium. Multilayer models can be produced quickly and cheaply and provide a strong sense of an object's 3D shape and texture over a wide range of viewing directions. Our method is designed to minimize visible cracks and other artifacts that can arise when projecting an input model onto a small number of parallel planes, and avoid layer transitions that cut the model along important surface features. We demonstrate multilayer models fabricated with glass and acrylic tiles using commercially available printers