Browsing by Author "Wang, Jun"

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    MODNet: Multi-offset Point Cloud Denoising Network Customized for Multi-scale Patches
    (The Eurographics Association and John Wiley & Sons Ltd., 2022) Huang, Anyi; Xie, Qian; Wang, Zhoutao; Lu, Dening; Wei, Mingqiang; Wang, Jun; Umetani, Nobuyuki; Wojtan, Chris; Vouga, Etienne
    The intricacy of 3D surfaces often results cutting-edge point cloud denoising (PCD) models in surface degradation including remnant noise, wrongly-removed geometric details. Although using multi-scale patches to encode the geometry of a point has become the common wisdom in PCD, we find that simple aggregation of extracted multi-scale features can not adaptively utilize the appropriate scale information according to the geometric information around noisy points. It leads to surface degradation, especially for points close to edges and points on complex curved surfaces. We raise an intriguing question - if employing multi-scale geometric perception information to guide the network to utilize multi-scale information, can eliminate the severe surface degradation problem? To answer it, we propose a Multi-offset Denoising Network (MODNet) customized for multi-scale patches. First, we extract the low-level feature of three scales patches by patch feature encoders. Second, a multi-scale perception module is designed to embed multi-scale geometric information for each scale feature and regress multi-scale weights to guide a multi-offset denoising displacement. Third, a multi-offset decoder regresses three scale offsets, which are guided by the multi-scale weights to predict the final displacement by weighting them adaptively. Experiments demonstrate that our method achieves new state-of-the-art performance on both synthetic and real-scanned datasets. Our code is publicly available at https://github.com/hay-001/MODNet.
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    SPCNet: Stepwise Point Cloud Completion Network
    (The Eurographics Association and John Wiley & Sons Ltd., 2022) Hu, Fei; Chen, Honghua; Lu, Xuequan; Zhu, Zhe; Wang, Jun; Wang, Weiming; Wang, Fu Lee; Wei, Mingqiang; Umetani, Nobuyuki; Wojtan, Chris; Vouga, Etienne
    How will you repair a physical object with large missings? You may first recover its global yet coarse shape and stepwise increase its local details. We are motivated to imitate the above physical repair procedure to address the point cloud completion task.We propose a novel stepwise point cloud completion network (SPCNet) for various 3D models with large missings. SPCNet has a hierarchical bottom-to-up network architecture. It fulfills shape completion in an iterative manner, which 1) first infers the global feature of the coarse result; 2) then infers the local feature with the aid of global feature; and 3) finally infers the detailed result with the help of local feature and coarse result. Beyond the wisdom of simulating the physical repair, we newly design a cycle loss to enhance the generalization and robustness of SPCNet. Extensive experiments clearly show the superiority of our SPCNet over the state-of-the-art methods on 3D point clouds with large missings. Code is available at https://github.com/1127368546/SPCNet.
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    UTOPIC: Uncertainty-aware Overlap Prediction Network for Partial Point Cloud Registration
    (The Eurographics Association and John Wiley & Sons Ltd., 2022) Chen, Zhilei; Chen, Honghua; Gong, Lina; Yan, Xuefeng; Wang, Jun; Guo, Yanwen; Qin, Jing; Wei, Mingqiang; Umetani, Nobuyuki; Wojtan, Chris; Vouga, Etienne
    High-confidence overlap prediction and accurate correspondences are critical for cutting-edge models to align paired point clouds in a partial-to-partial manner. However, there inherently exists uncertainty between the overlapping and non-overlapping regions, which has always been neglected and significantly affects the registration performance. Beyond the current wisdom, we propose a novel uncertainty-aware overlap prediction network, dubbed UTOPIC, to tackle the ambiguous overlap prediction problem; to our knowledge, this is the first to explicitly introduce overlap uncertainty to point cloud registration. Moreover, we induce the feature extractor to implicitly perceive the shape knowledge through a completion decoder, and present a geometric relation embedding for Transformer to obtain transformation-invariant geometry-aware feature representations.With the merits of more reliable overlap scores and more precise dense correspondences, UTOPIC can achieve stable and accurate registration results, even for the inputs with limited overlapping areas. Extensive quantitative and qualitative experiments on synthetic and real benchmarks demonstrate the superiority of our approach over state-of-the-art methods.