Browsing by Author "Yan, Xuefeng"

Now showing 1 - 4 of 4
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    Contrastive Semantic-Guided Image Smoothing Network
    (The Eurographics Association and John Wiley & Sons Ltd., 2022) Wang, Jie; Wang, Yongzhen; Feng, Yidan; Gong, Lina; Yan, Xuefeng; Xie, Haoran; Wang, Fu Lee; Wei, Mingqiang; Umetani, Nobuyuki; Wojtan, Chris; Vouga, Etienne
    Image smoothing is a fundamental low-level vision task that aims to preserve salient structures of an image while removing insignificant details. Deep learning has been explored in image smoothing to deal with the complex entanglement of semantic structures and trivial details. However, current methods neglect two important facts in smoothing: 1) naive pixel-level regression supervised by the limited number of high-quality smoothing ground-truth could lead to domain shift and cause generalization problems towards real-world images; 2) texture appearance is closely related to object semantics, so that image smoothing requires awareness of semantic difference to apply adaptive smoothing strengths. To address these issues, we propose a novel Contrastive Semantic-Guided Image Smoothing Network (CSGIS-Net) that combines both contrastive prior and semantic prior to facilitate robust image smoothing. The supervision signal is augmented by leveraging undesired smoothing effects as negative teachers, and by incorporating segmentation tasks to encourage semantic distinctiveness. To realize the proposed network, we also enrich the original VOC dataset with texture enhancement and smoothing labels, namely VOC-smooth, which first bridges image smoothing and semantic segmentation. Extensive experiments demonstrate that the proposed CSGIS-Net outperforms state-of-the-art algorithms by a large margin. Code and dataset are available at https://github.com/wangjie6866/CSGIS-Net.
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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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    TogetherNet: Bridging Image Restoration and Object Detection Together via Dynamic Enhancement Learning
    (The Eurographics Association and John Wiley & Sons Ltd., 2022) Wang, Yongzhen; Yan, Xuefeng; Zhang, Kaiwen; Gong, Lina; Xie, Haoran; Wang, Fu Lee; Wei, Mingqiang; Umetani, Nobuyuki; Wojtan, Chris; Vouga, Etienne
    Adverse weather conditions such as haze, rain, and snow often impair the quality of captured images, causing detection networks trained on normal images to generalize poorly in these scenarios. In this paper, we raise an intriguing question - if the combination of image restoration and object detection, can boost the performance of cutting-edge detectors in adverse weather conditions. To answer it, we propose an effective yet unified detection paradigm that bridges these two subtasks together via dynamic enhancement learning to discern objects in adverse weather conditions, called TogetherNet. Different from existing efforts that intuitively apply image dehazing/deraining as a pre-processing step, TogetherNet considers a multi-task joint learning problem. Following the joint learning scheme, clean features produced by the restoration network can be shared to learn better object detection in the detection network, thus helping TogetherNet enhance the detection capacity in adverse weather conditions. Besides the joint learning architecture, we design a new Dynamic Transformer Feature Enhancement module to improve the feature extraction and representation capabilities of TogetherNet. Extensive experiments on both synthetic and real-world datasets demonstrate that our TogetherNet outperforms the state-of-the-art detection approaches by a large margin both quantitatively and qualitatively. Source code is available at https://github.com/yz-wang/TogetherNet.
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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.