3DMV: Learning 3D with Multi-View Supervision
CVPR 2023 WorkshopCall for papers: February 27th Submission Deadline: April 9th Workshop Day: June 19th
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 the images by CNNs/transformers/diffusion to obtain a more descriptive representation for the 3D data. However, several challenges remain in this multi-view direction, including handling the intersection with other modalities like point clouds and meshes and addressing some of the problems that affect 2D projections like occlusion and view-point selection. We aim to enhance the synergy between multi-view research across different tasks by inviting keynote speakers from across the spectrum of 3D understanding and generation, mixing essential 3D topics (like multi-view stereo) with modern generation techniques ( like NeRFs). The detailed topics covered in the workshop include the following:
We are soliciting papers that use Multi-view deep learning to address problems in 3D Understanding and 3D Generation, including but not limited to the following topics:
| Time | Session | Speakers | Recordings |
| {{tableData[currentCountry][0]}} | Opening Remarks | Abdullah Hamdi | link (6:00) |
| {{tableData[currentCountry][1]}} | Multi-view for 3D Generation | Ben Poole | link (19:07) |
| {{tableData[currentCountry][2]}} | 3D Perception from Multi-X | Charles Qi | link (57:28) |
| {{tableData[currentCountry][3]}} | Break - Poster Session | ||
| {{tableData[currentCountry][4]}} | 3D Recognition in the Wild With and Without 3D Supervision | Georgia Gkioxari | not available |
| {{tableData[currentCountry][5]}} | Photorealistic Reconstruction with Neural Parametric Models | Matthias Niessner | link (00:00) |
| {{tableData[currentCountry][6]}} | Panel Discussion (includes all speakers) | Abdullah Hamdi | - |
