ScanComplete: Large-Scale Scene Completion and Semantic Segmentation for 3D ScansĪ. VirtualHome: Simulating Household Activities via Programs SavareseĬomputer Vision and Pattern Recognition (CVPR), 2018 IEEE Conference on, IEEE, 2018 Gibson env: real-world perception for embodied agentsį. GRAINS: Generative Recursive Autoencoders for INdoor Scenes Fast and Flexible Indoor Scene Synthesis via Deep Convolutional Generative Models Invited Talk 8: Siddhartha Chaudhuri - "Recursive neural networks for scene synthesis" Invited Talk 7: Kristen Grauman - "Learning to explore 3D scenes" Invited Talk 5: Ellie Pavlick - "Natural Language Understanding: Where we are stuck and where you can help" Schönberger - "3D Scene Reconstruction from Unstructured Imagery" Invited Talk 2: Angela Dai - "From unstructured range scans to 3d models"Ĭoffee Break and Poster Session (Pacific Arena Ballroom, #24-#33) Please submit your paper to the following address by the deadline: mention in your email if your submission has already been accepted for publication (and the name of the conference). We also welcome already published papers that are within the scope of the workshop (without re-formatting), including papers from the main CVPR conference. The submission should be in the CVPR format.Īccepted extended abstracts will be made publicly available as non-archival reports, allowing future submissions to archival conferences or journals. Submission: we encourage submissions of up to 6 pages excluding references and acknowledgements. Use of 3D scenes for simulation targeted to learning in computer vision, robotics, and cognitive science.Learning from real world data for improved models of virtual worlds.Completion of 3D scenes or objects in 3D scenes.3D scene understanding based on synthetic 3D scene data.Synthesis of 3D scenes from sensor inputs (e.g., images, videos, or scans).Generative models for 3D scene synthesis.Paper topics may include but are not limited to: Vision tasks that consume such data include automatic scene classification and segmentation, 3D reconstruction, human activity recognition, robotic visual navigation, and more.Ĭall for papers: We invite extended abstracts for work on tasks related to 3D scene generation or tasks leveraging generated 3D scenes. images) or from high-level specifications (e.g. We define "generation of 3D environments" to include methods that generate 3D scenes from sensory inputs (e.g. In this workshop, we aim to bring together researchers working on automatic generation of 3D environments for computer vision research with researchers who are making use of 3D environment data for a variety of computer vision tasks. To enable large-scale embodied visual learning in 3D environments, we must go beyond such static datasets and instead pursue the automatic synthesis of novel, task-relevant virtual environments. While these existing datasets are a valuable resource, they are also finite in size and don't adapt to the needs of different vision tasks. The community has recently benefited from large scale datasets of both synthetic 3D environments and reconstructions of real spaces, and the development of 3D simulation frameworks for studying embodied agents. Furthermore, AI/vision/robotics researchers are also turning to virtual environments to train data-hungry models for tasks such as visual navigation, 3D reconstruction, activity recognition, and more.Īs the vision community turns from passive internet-images-based vision tasks to applications such as the ones listed above, the need for virtual 3D environments becomes critical. Game developers, VR/AR designers, architects, and interior design firms are all increasingly making use virtual 3D scenes for prototyping and final products. People spend a large percentage of their lives indoors-in bedrooms, living rooms, offices, kitchens, and other such spaces-and the demand for virtual versions of these real-world spaces has never been higher.
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