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

enterprise

abnegate

Helena Handschuh,Anna Lysyanskayam the raw point clouds for 3D object detection, most previous researches utilize PointNet and its variants as the feature learning backbone and have seen encouraging results. However, these methods capture point features independently without modeling the interaction between points, and simple symme

鋼筆記下懲罰

Arthur Lazzaretti,Charalampos Papamanthouity of existing networks are relatively fixed, which makes it difficult for them to be flexibly applied to devices with different computational constraints. Instead of manually designing the network structure for each specific device, in this paper, we propose a novel training-free neural architectu

COW

blister

思考

EAI-Stereo: Error Aware Iterative Network for?Stereo Matchingwork which could carry more semantic information across iterations. We demonstrate the efficiency and effectiveness of our method on KITTI 2015, Middlebury, and ETH3D. At the time of writing this paper, EAI-Stereo ranks . on the Middlebury leaderboard and . on the ETH3D Stereo benchmark for 50% quan

鳴叫

Temporal-Aware Siamese Tracker: Integrate Temporal Context for?3D Object Trackingate the tracking quality of the template so that the high-quality templates are collected to form the historical template set. Then, with the initial feature embeddings of the historical templates, the temporal feature enhancement module concatenates all template embeddings as a whole and then feeds

退潮

Neural Plenoptic Sampling: Learning Light-Field from?Thousands of?Imaginary Eyesic function at every position in the space of interest. By placing virtual viewpoints (dubbed ‘imaginary eyes’) at thousands of randomly sampled locations and leveraging multi-view geometric relationship, we train the MLP to regress the plenoptic function for the space. Our network is trained on a p

救護(hù)車

香料

NEO-3DF: Novel Editing-Oriented 3D Face Creation and?Reconstruction with the original 2D image. Experiments show that the results of NEO-3DF outperform existing methods in intuitive face editing and have better 3D-to-2D alignment accuracy (14% higher IoU) than global face model-based reconstruction. Code available at ..