41-Issue 7

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https://diglib.eg.org/handle/10.2312/2633207

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Browsing 41-Issue 7 by Subject "based models"

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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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    SIGDT: 2D Curve Reconstruction
    (The Eurographics Association and John Wiley & Sons Ltd., 2022) Marin, Diana; Ohrhallinger, Stefan; Wimmer, Michael; Umetani, Nobuyuki; Wojtan, Chris; Vouga, Etienne
    Determining connectivity between points and reconstructing their shape boundaries are long-standing problems in computer graphics. One possible approach to solve these problems is to use a proximity graph. We propose a new proximity graph computed by intersecting the to-date rarely used proximity-based graph called spheres-of-influence graph (SIG) with the Delaunay triangulation (DT). We prove that the resulting graph, which we name SIGDT, contains the piece-wise linear reconstruction for a set of unstructured points in the plane for a sampling condition superseding current bounds and capturing well practical point sets' properties. As an application, we apply a dual of boundary adjustment steps from the CONNECT2D algorithm to remove the redundant edges. We show that the resulting algorithm SIG-CONNECT2D yields the best reconstruction accuracy compared to state-of-the-art algorithms from a recent comprehensive benchmark, and the method offers the potential for further improvements, e.g., for surface reconstruction.
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    SO(3)-Pose: SO(3)-Equivariance Learning for 6D Object Pose Estimation
    (The Eurographics Association and John Wiley & Sons Ltd., 2022) Pan, Haoran; Zhou, Jun; Liu, Yuanpeng; Lu, Xuequan; Wang, Weiming; Yan, Xuefeng; Wei, Mingqiang; Umetani, Nobuyuki; Wojtan, Chris; Vouga, Etienne
    6D pose estimation of rigid objects from RGB-D images is crucial for object grasping and manipulation in robotics. Although RGB channels and the depth (D) channel are often complementary, providing respectively the appearance and geometry information, it is still non-trivial on how to fully benefit from the two cross-modal data. From the simple yet new observation, when an object rotates, its semantic label is invariant to the pose while its keypoint offset direction is variant to the pose. To this end, we present SO(3)-Pose, a new representation learning network to explore SO(3)-equivariant and SO(3)-invariant features from the depth channel for pose estimation. The SO(3)-invariant features facilitate to learn more distinctive representations for segmenting objects with similar appearance from RGB channels. The SO(3)-equivariant features communicate with RGB features to deduce the (missed) geometry for detecting keypoints of an object with the reflective surface from the depth channel. Unlike most of existing pose estimation methods, our SO(3)-Pose not only implements the information communication between the RGB and depth channels, but also naturally absorbs the SO(3)-equivariance geometry knowledge from depth images, leading to better appearance and geometry representation learning. Comprehensive experiments show that our method achieves the stateof- the-art performance on three benchmarks. Code is available at https://github.com/phaoran9999/SO3-Pose.
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    USTNet: Unsupervised Shape-to-Shape Translation via Disentangled Representations
    (The Eurographics Association and John Wiley & Sons Ltd., 2022) Wang, Haoran; Li, Jiaxin; Telea, Alexandru; Kosinka, Jirí; Wu, Zizhao; Umetani, Nobuyuki; Wojtan, Chris; Vouga, Etienne
    We propose USTNet, a novel deep learning approach designed for learning shape-to-shape translation from unpaired domains in an unsupervised manner. The core of our approach lies in disentangled representation learning that factors out the discriminative features of 3D shapes into content and style codes. Given input shapes from multiple domains, USTNet disentangles their representation into style codes that contain distinctive traits across domains and content codes that contain domaininvariant traits. By fusing the style and content codes of the target and source shapes, our method enables us to synthesize new shapes that resemble the target style and retain the content features of source shapes. Based on the shared style space, our method facilitates shape interpolation by manipulating the style attributes from different domains. Furthermore, by extending the basic building blocks of our network from two-class to multi-class classification, we adapt USTNet to tackle multi-domain shape-to-shape translation. Experimental results show that our approach can generate realistic and natural translated shapes and that our method leads to improved quantitative evaluation metric results compared to 3DSNet. Codes are available at https://Haoran226.github.io/USTNet.
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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.