Browsing by Author "Wang, Weiming"

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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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    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.