Piecewise-Planar 3D Reconstruction with Edge and Corner Regularization

International audienceThis paper presents a method for the 3D reconstruction of a piecewise-planar surface from range images, typi-cally laser scans with millions of points. The reconstructed surface is a watertight polygonal mesh that conforms to observations at a given scale in the visible planar parts of the scene, and that is plausible in hidden parts. We formulate surface reconstruction as a discrete optimization problem based on detected and hypothesized planes. One of our major contributions, besides a treatment of data anisotropy and novel surface hypotheses, is a regu-larization of the reconstructed surface w.r.t. the length of edges and the number of corners. Compared to classical area-based regularization, it better captures surface complexity and is therefore better suited for man-made en-vironments, such as buildings. To handle the underlying higher-order potentials, that are problematic for MRF optimizers, we formulate minimization as a sparse mixed-integer linear programming problem and obtain an ap-proximate solution using a simple relaxation. Experiments show that it is fast and reaches near-optimal solutions

A method to improve corner detectors (Harris, Shi-Tomasi & FAST) using adaptive contrast enhancement filter

A method to improve interested-points detectors in an image that suffers from the problem of illumination was conducted in this paper. Three algorithms are adopted based on Harris, Shi-Tomasi, and FAST algorithms to identify the interested-points in images that are required to match, recognize and track objects in the digital images. Detecting the interested-points in images with bad illumination is one of the most challenging tasks in the field of image processing. The illumination is considered as one of the main causes of damage of the natural images during the acquisition and transition. Detecting the interested-points of these images doesn’t give the desired results, which is why handling this problem for those images is very important. The Adaptive Contrast Enhancement Filter approach is applied for solving this problem

Plane-Based Optimization of Geometry and Texture for RGB-D Reconstruction of Indoor Scenes

We present a novel approach to reconstruct RGB-D indoor scene with plane primitives. Our approach takes as input a RGB-D sequence and a dense coarse mesh reconstructed by some 3D reconstruction method on the sequence, and generate a lightweight, low-polygonal mesh with clear face textures and sharp features without losing geometry details from the original scene. To achieve this, we firstly partition the input mesh with plane primitives, simplify it into a lightweight mesh next, then optimize plane parameters, camera poses and texture colors to maximize the photometric consistency across frames, and finally optimize mesh geometry to maximize consistency between geometry and planes. Compared to existing planar reconstruction methods which only cover large planar regions in the scene, our method builds the entire scene by adaptive planes without losing geometry details and preserves sharp features in the final mesh. We demonstrate the effectiveness of our approach by applying it onto several RGB-D scans and comparing it to other state-of-the-art reconstruction methods.Comment: in International Conference on 3D Vision 2018; Models and Code: see https://github.com/chaowang15/plane-opt-rgbd. arXiv admin note: text overlap with arXiv:1905.0885

Planar Shape Detection at Structural Scales

International audienceInterpreting 3D data such as point clouds or surface meshes depends heavily on the scale of observation. Yet, existing algorithms for shape detection rely on trial-and-error parameter tunings to output configurations representative of a structural scale. We present a framework to automatically extract a set of representations that capture the shape and structure of man-made objects at different key abstraction levels. A shape-collapsing process first generates a fine-to-coarse sequence of shape representations by exploiting local planarity. This sequence is then analyzed to identify significant geometric variations between successive representations through a supervised energy minimization. Our framework is flexible enough to learn how to detect both existing structural formalisms such as the CityGML Levels Of Details, and expert-specified levels of abstraction. Experiments on different input data and classes of man-made objects, as well as comparisons with existing shape detection methods, illustrate the strengths of our approach in terms of efficiency and flexibility

Classification d’objets par abstraction planaire

We present a supervised machine learning approach for classification of objects from sampled point data. The main idea consists in first abstracting the input object into planar parts at several scales, then discriminate between the different classes of objects solely through features derived from these planar shapes. Abstracting into planar shapes provides a means to both reduce the computational complexity and improve robustness to defects inherent to the acquisition process. Measuring statistical properties and relationships between planar shapes offers invariance to scale and orientation. A random forest is then used for solving the multiclass classification problem. We demonstrate the potential of our approach on a set of indoor objects from the Princeton shape benchmark and on objects acquired from indoor scenes and compare the performance of our method with other point-based shape descriptors.Nous introduisons une approche par apprentissage supervisée pour classifier des objetsà partir de points échantillonnés dans l’espace. L’idée principale consiste à approximer l’objet initialen parties planaires à différentes échelles, pour ensuite distinguer les différentes classes d’objets sanstenir compte des points échantillonnées. L’abstraction en formes planaires est un moyen à la fois deréduire la complexité algorithmique de l’analyse, et d’améliorer la robustesse aux défauts de mesuresdans le processus d’acquisition des données. Mesurer des propriétés statistiques et des relations entreformes planaires offre une invariance à l’échelle et à l’orientation. L’algorithme Random Forest estutilisé pour résoudre le problème de classification multi-classe. Nous démontrons le potentiel de notreapproche sur un ensemble d’objet de scène d’intérieur en utilisant plusieurs benchmarks et en comparantles performances avec des méthodes basées sur des descripteurs locaux de points

Object classification via planar abstraction

International audienceWe present a supervised machine learning approach for classification of objects from sampled point data. The main idea consists in first abstracting the input object into planar parts at several scales, then discriminate between the different classes of objects solely through features derived from these planar shapes. Abstracting into planar shapes provides a means to both reduce the computational complexity and improve robustness to defects inherent to the acquisition process. Measuring statistical properties and relationships between planar shapes offers invariance to scale and orientation. A random forest is then used for solving the multiclass classification problem. We demonstrate the potential of our approach on a set of indoor objects from the Princeton shape benchmark and on objects acquired from indoor scenes and compare the performance of our method with other point-based shape descriptors

PIECEWISE-PLANAR APPROXIMATION OF LARGE 3D DATA AS GRAPH-STRUCTURED OPTIMIZATION

We introduce a new method for the piecewise-planar approximation of 3D data, including point clouds and meshes. Our method is designed to operate on large datasets (e.g. millions of vertices) containing planar structures, which are very frequent in anthropic scenes. Our approach is also adaptive to the local geometric complexity of the input data. Our main contribution is the formulation of the piecewise-planar approximation problem as a non-convex optimization problem. In turn, this problem can be efficiently solved with a graph-structured working set approach. We compare our results with a state-of-the-art region-growing-based segmentation method and show a significant improvement both in terms of approximation error and computation efficiency