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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書目名稱Computer Vision – ACCV 2022影響因子(影響力)




書目名稱Computer Vision – ACCV 2022影響因子(影響力)學(xué)科排名




書目名稱Computer Vision – ACCV 2022網(wǎng)絡(luò)公開度




書目名稱Computer Vision – ACCV 2022網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Computer Vision – ACCV 2022被引頻次




書目名稱Computer Vision – ACCV 2022被引頻次學(xué)科排名




書目名稱Computer Vision – ACCV 2022年度引用




書目名稱Computer Vision – ACCV 2022年度引用學(xué)科排名




書目名稱Computer Vision – ACCV 2022讀者反饋




書目名稱Computer Vision – ACCV 2022讀者反饋學(xué)科排名

Neutropenia

Temporal-Aware Siamese Tracker: Integrate Temporal Context for?3D Object Trackingnt Siamese trackers focus on aggregating the target information from the latest template into the search area for target-specific feature construction, which presents the limited performance in the case of object occlusion or object missing. To this end, in this paper, we propose a novel temporal-aw

Affiliation

西瓜

Override

NEO-3DF: Novel Editing-Oriented 3D Face Creation and?Reconstruction user might wish to edit the reconstructed 3D face, but 3D face editing has seldom been studied. This paper presents such method and shows that reconstruction and editing can help each other. In the presented framework named NEO-3DF, the 3D face model we propose has independent sub-models correspond

燒烤

LSMD-Net: LiDAR-Stereo Fusion with?Mixture Density Network for?Depth Sensinghe stereo camera sensor can provide dense depth prediction but underperforms in texture-less, repetitive and occlusion areas while the LiDAR sensor can generate accurate measurements but results in sparse map. In this paper, we advocate to fuse LiDAR and stereo camera for accurate dense depth sensin

燒烤

Point Cloud Upsampling via?Cascaded Refinement Network by carefully designing a single-stage network, which makes it still challenging to generate a high-fidelity point distribution. Instead, upsampling point cloud in a coarse-to-fine manner is a decent solution. However, existing coarse-to-fine upsampling methods require extra training strategies, whi

OATH

阻擋

Vectorizing Building Blueprintslueprint. A state-of-the-art floorplan vectorization algorithm starts by detecting corners, whose process does not scale to high-definition floorplans with thin interior walls, small door frames, and long exterior walls. Our approach 1) obtains rough semantic segmentation by running off-the-shelf se

nitric-oxide