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Titlebook: Computer Vision – ACCV 2022; 16th Asian Conferenc Lei Wang,Juergen Gall,Rama Chellappa Conference proceedings 2023 The Editor(s) (if applic

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51#
發(fā)表于 2025-3-30 11:07:41 | 只看該作者
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發(fā)表于 2025-3-30 13:14:40 | 只看該作者
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發(fā)表于 2025-3-30 17:50:03 | 只看該作者
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發(fā)表于 2025-3-30 21:31:44 | 只看該作者
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發(fā)表于 2025-3-31 04:21:45 | 只看該作者
Learning Inter-superpoint Affinity for?Weakly Supervised 3D Instance Segmentationplying volume constraints of objects in clustering on the superpoint graph. Extensive experiments on the ScanNet-v2 and S3DIS datasets demonstrate that our method achieves state-of-the-art performance in the weakly supervised point cloud instance segmentation task, and even outperforms some fully su
56#
發(fā)表于 2025-3-31 06:12:34 | 只看該作者
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發(fā)表于 2025-3-31 12:52:26 | 只看該作者
SymmNeRF: Learning to?Explore Symmetry Prior for?Single-View View Synthesisd by a hypernetwork. As the parameters are conditioned on the image-encoded latent codes, SymmNeRF is thus scene-independent and can generalize to new scenes. Experiments on synthetic and real-world datasets show that SymmNeRF synthesizes novel views with more details regardless of the pose transfor
58#
發(fā)表于 2025-3-31 15:10:37 | 只看該作者
Meta-Det3D: Learn to?Learn Few-Shot 3D Object Detectionmples to characterize the task information, one for each distinct object class. Each re-weighting vector performs channel-wise attention to the candidate features to re-calibrate the query object features, adapting them to detect objects of the same classes. Finally, the adapted features are fed int
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發(fā)表于 2025-3-31 20:25:51 | 只看該作者
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