Browsing by Author "Mura, Claudio"

Now showing 1 - 5 of 5
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    Multi-Scale Point Cloud Analysis
    (The Eurographics Association, 2019) Lejemble, Thibault; Mura, Claudio; Barthe, Loïc; Mellado, Nicolas; Fusiello, Andrea and Bimber, Oliver
    Surfaces sampled with point clouds often exhibit multi-scale properties due to the high variation between their feature size. Traditional shape analysis techniques usually rely on geometric descriptors able to characterize a point and its close neighborhood at multiple scale using smoothing kernels of varying radii. We propose to add a spatial regularization to these point-wise descriptors in two different ways. The first groups similar points in regions that are structured in a hierarchical graph. The graph is then simplified and processed to extract pertinent regions. The second performs a spatial gradient descent in order to highlight stable parts of the surface. We show two experiments focusing on planar and anisotropic feature areas respectively.
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    Persistence Analysis of Multi-scale Planar Structure Graph in Point Clouds
    (The Eurographics Association and John Wiley & Sons Ltd., 2020) Lejemble, Thibault; Mura, Claudio; Barthe, Loïc; Mellado, Nicolas; Panozzo, Daniele and Assarsson, Ulf
    Modern acquisition techniques generate detailed point clouds that sample complex geometries. For instance, we are able to produce millimeter-scale acquisition of whole buildings. Processing and exploring geometrical information within such point clouds requires scalability, robustness to acquisition defects and the ability to model shapes at different scales. In this work, we propose a new representation that enriches point clouds with a multi-scale planar structure graph. We define the graph nodes as regions computed with planar segmentations at increasing scales and the graph edges connect regions that are similar across scales. Connected components of the graph define the planar structures present in the point cloud within a scale interval. For instance, with this information, any point is associated to one or several planar structures existing at different scales. We then use topological data analysis to filter the graph and provide the most prominent planar structures. Our representation naturally encodes a large range of information. We show how to efficiently extract geometrical details (e.g. tiles of a roof), arrangements of simple shapes (e.g. steps and mean ramp of a staircase), and large-scale planar proxies (e.g. walls of a building) and present several interactive tools to visualize, select and reconstruct planar primitives directly from raw point clouds. The effectiveness of our approach is demonstrated by an extensive evaluation on a variety of input data, as well as by comparing against state-of-the-art techniques and by showing applications to polygonal mesh reconstruction.
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    A Robust Feature-aware Sparse Mesh Representation
    (The Eurographics Association, 2020) Fuentes Perez, Lizeth Joseline; Romero Calla, Luciano Arnaldo; Montenegro, Anselmo Antunes; Mura, Claudio; Pajarola, Renato; Lee, Sung-hee and Zollmann, Stefanie and Okabe, Makoto and Wuensche, Burkhard
    The sparse representation of signals defined on Euclidean domains has been successfully applied in signal processing. Bringing the power of sparse representations to non-regular domains is still a challenge, but promising approaches have started emerging recently. In this paper, we investigate the problem of sparsely representing discrete surfaces and propose a new representation that is capable of providing tools for solving different geometry processing problems. The sparse discrete surface representation is obtained by combining innovative approaches into an integrated method. First, to deal with irregular mesh domains, we devised a new way to subdivide discrete meshes into a set of patches using a feature-aware seed sampling. Second, we achieve good surface approximation with over-fitting control by combining the power of a continuous global dictionary representation with a modified Orthogonal Marching Pursuit. The discrete surface approximation results produced were able to preserve the shape features while being robust to over-fitting. Our results show that the method is quite promising for applications like surface re-sampling and mesh compression.
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    State-of-the-art in Automatic 3D Reconstruction of Structured Indoor Environments
    (The Eurographics Association and John Wiley & Sons Ltd., 2020) Pintore, Giovanni; Mura, Claudio; Ganovelli, Fabio; Fuentes-Perez, Lizeth Joseline; Pajarola, Renato; Gobbetti, Enrico; Mantiuk, Rafal and Sundstedt, Veronica
    Creating high-level structured 3D models of real-world indoor scenes from captured data is a fundamental task which has important applications in many fields. Given the complexity and variability of interior environments and the need to cope with noisy and partial captured data, many open research problems remain, despite the substantial progress made in the past decade. In this survey, we provide an up-to-date integrative view of the field, bridging complementary views coming from computer graphics and computer vision. After providing a characterization of input sources, we define the structure of output models and the priors exploited to bridge the gap between imperfect sources and desired output. We then identify and discuss the main components of a structured reconstruction pipeline, and review how they are combined in scalable solutions working at the building level. We finally point out relevant research issues and analyze research trends.
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    Walk2Map: Extracting Floor Plans from Indoor Walk Trajectories
    (The Eurographics Association and John Wiley & Sons Ltd., 2021) Mura, Claudio; Pajarola, Renato; Schindler, Konrad; Mitra, Niloy; Mitra, Niloy and Viola, Ivan
    Recent years have seen a proliferation of new digital products for the efficient management of indoor spaces, with important applications like emergency management, virtual property showcasing and interior design. While highly innovative and effective, these products rely on accurate 3D models of the environments considered, including information on both architectural and non-permanent elements. These models must be created from measured data such as RGB-D images or 3D point clouds, whose capture and consolidation involves lengthy data workflows. This strongly limits the rate at which 3D models can be produced, preventing the adoption of many digital services for indoor space management. We provide a radical alternative to such data-intensive procedures by presentingWalk2Map, a data-driven approach to generate floor plans only from trajectories of a person walking inside the rooms. Thanks to recent advances in data-driven inertial odometry, such minimalistic input data can be acquired from the IMU readings of consumer-level smartphones, which allows for an effortless and scalable mapping of real-world indoor spaces. Our work is based on learning the latent relation between an indoor walk trajectory and the information represented in a floor plan: interior space footprint, portals, and furniture. We distinguish between recovering area-related (interior footprint, furniture) and wall-related (doors) information and use two different neural architectures for the two tasks: an image-based Encoder-Decoder and a Graph Convolutional Network, respectively. We train our networks using scanned 3D indoor models and apply them in a cascaded fashion on an indoor walk trajectory at inference time. We perform a qualitative and quantitative evaluation using both trajectories simulated from scanned models of interiors and measured, real-world trajectories, and compare against a baseline method for image-to-image translation. The experiments confirm that our technique is viable and allows recovering reliable floor plans from minimal walk trajectory data.