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3DMV

3dmv2023.github.io · 1,417 words · saved by 1 readers

This workshop is for multi-view deep learning. It covers various topics that involve multi-view deep learning for core 3D understanding tasks (recognition, detection, segmentation) and methods that use posed or un-posed multi-view images for 3D reconstruction and generation. Many of the recent advances in 3D vision have focused on the direct approach of applying deep learning to 3D data (e.g., 3D point clouds, meshes, voxels ). Another way of using deep learning for 3D understanding is to project 3D into multiple 2D images and apply 2D networks to process the 3D data indirectly. Tackling 3D vision tasks with indirect approaches has two main advantages: (i) mature and transferable 2D computer vision models (CNNs, Transformers, Diffusion, etc.), and (ii) large and diverse labeled image datasets for pre-training (e.g., ImageNet). Furthermore, recent advances in differentiable rendering allow for end-to-end deep learning pipelines that render multi-view images of the 3D data and process th

3DMV 3DMV: Learning 3D with Multi-View Supervision CVPR 2023 Workshop Call for papers: February 27th Submission Deadline: April 9th Workshop Day: June 19th Event Pictures --> Learning 3D with Multi-View Supervision @ CVPR Workshop 2023 This workshop is for multi-view deep learning. It covers various topics that involve multi-view deep learning for core 3D understanding tasks (recognition, detection, segmentation) and methods that use posed or un-posed multi-view images for 3D reconstruction and generation. Many of the recent advances in 3D vision have focused on the direct approach of applying

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