Volume 42 (2023)

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Browsing Volume 42 (2023) by Subject "based models"

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    Lightweight Curvature Estimation on Point Clouds with Randomized Corrected Curvature Measures
    (The Eurographics Association and John Wiley & Sons Ltd., 2023) Lachaud, Jacques-Olivier; Coeurjolly, David; Labart, Céline; Romon, Pascal; Thibert, Boris; Memari, Pooran; Solomon, Justin
    The estimation of differential quantities on oriented point cloud is a classical step for many geometry processing tasks in computer graphics and vision. Even if many solutions exist to estimate such quantities, they usually fail at satisfying both a stable estimation with theoretical guarantee, and the efficiency of the associated algorithm. Relying on the notion of corrected curvature measures [LRT22, LRTC20] designed for surfaces, the method introduced in this paper meets both requirements. Given a point of interest and a few nearest neighbours, our method estimates the whole curvature tensor information by generating random triangles within these neighbours and normalising the corrected curvature measures by the corrected area measure. We provide a stability theorem showing that our pointwise curvatures are accurate and convergent, provided the noise in position and normal information has a variance smaller than the radius of neighbourhood. Experiments and comparisons with the state-of-the-art confirm that our approach is more accurate and much faster than alternatives. The method is fully parallelizable, requires only one nearest neighbour request per point of computation, and is trivial to implement.
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    Robust Pointset Denoising of Piecewise-Smooth Surfaces through Line Processes
    (The Eurographics Association and John Wiley & Sons Ltd., 2023) Wei, Jiayi; Chen, Jiong; Rohmer, Damien; Memari, Pooran; Desbrun, Mathieu; Myszkowski, Karol; Niessner, Matthias
    Denoising is a common, yet critical operation in geometry processing aiming at recovering high-fidelity models of piecewisesmooth objects from noise-corrupted pointsets. Despite a sizable literature on the topic, there is a dearth of approaches capable of processing very noisy and outlier-ridden input pointsets for which no normal estimates and no assumptions on the underlying geometric features or noise type are provided. In this paper, we propose a new robust-statistics approach to denoising pointsets based on line processes to offer robustness to noise and outliers while preserving sharp features possibly present in the data. While the use of robust statistics in denoising is hardly new, most approaches rely on prescribed filtering using data-independent blending expressions based on the spatial and normal closeness of samples. Instead, our approach deduces a geometric denoising strategy through robust and regularized tangent plane fitting of the initial pointset, obtained numerically via alternating minimizations for efficiency and reliability. Key to our variational approach is the use of line processes to identify inliers vs. outliers, as well as the presence of sharp features. We demonstrate that our method can denoise sampled piecewise-smooth surfaces for levels of noise and outliers at which previous works fall short.
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    Structure Learning for 3D Point Cloud Generation from Single RGB Images
    (The Eurographics Association and John Wiley & Sons Ltd., 2023) Charrada, Tarek Ben; Laga, Hamid; Tabia, Hedi; Chaine, Raphaëlle; Deng, Zhigang; Kim, Min H.
    3D point clouds can represent complex 3D objects of arbitrary topologies and with fine-grained details. They are, however, hard to regress from images using convolutional neural networks, making tasks such as 3D reconstruction from monocular RGB images challenging. In fact, unlike images and volumetric grids, point clouds are unstructured and thus lack proper parameterization, which makes them difficult to process using convolutional operations. Existing point-based 3D reconstruction methods that tried to address this problem rely on complex end-to-end architectures with high computational costs. Instead, we propose in this paper a novel mechanism that decouples the 3D reconstruction problem from the structure (or parameterization) learning task, making the 3D reconstruction of objects of arbitrary topologies tractable and thus easier to learn. We achieve this using a novel Teacher-Student network where the Teacher learns to structure the point clouds. The Student then harnesses the knowledge learned by the Teacher to efficiently regress accurate 3D point clouds. We train the Teacher network using 3D ground-truth supervision and the Student network using the Teacher’'s annotations. Finally, we employ a novel refinement network to overcome the upper-bound performance that is set by the Teacher network. Our extensive experiments on ShapeNet and Pix3D benchmarks, and on in-the-wild images demonstrate that the proposed approach outperforms previous methods in terms of reconstruction accuracy and visual quality.