First Workshop on Photorealistic Image and Environment Synthesis
for Computer Vision (PIES-CV)
To be held as a hybrid workshop in conjunction with IEEE/CVF WACV 2023.
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First Workshop on Photorealistic Image and Environment Synthesis
Promoting the synthesis of photorealistic images and virtual environments for research purposes.
Mission of the project
Mission of the project
The First Workshop on Photorealistic Image and Environment Synthesis for Computer Vision (PIES-CV) will engage experts and researchers on the synthesis of photorealistic images and virtual environments, particularly in the form of public datasets, software tools, and infrastructures, for computer vision (CV) research. Such public datasets, software tools, and infrastructures will enable researchers to better investigate how photorealism affects CV algorithms and approaches. Photorealistic image and environment synthesis can benefit multiple research areas in addition to CV, such as machine learning, robotics, human perception, multimedia systems, and mixed reality.
Schedule
Schedule
Note: PIES-CV will be held as a hybrid workshop.
Organizers
Organizers
Nicholas R. Gans, University of Texas at Arlington
Ryan P. McMahan, University of Central Florida
List of Invited Speakers
List of Invited Speakers
Keynote Speaker
Keynote Speaker
Angel Chang - Creating Realistic and Interactive 3D Environments
Other Invited Speakers
Other Invited Speakers
William Beksi - Synthetic 3D Datasets for Indoor Robotic Applications
Stefan Leutenegger - Interiornet: Mega-scale Multi-sensor Photo-realistic Indoor Scenes Dataset
Matthias Nießner - The Revolution of Neural Rendering
Hamid Rezatofighi - Jack Rabbot Dataset and Benchmark (JRDB)
Katja Schwartz - 3D-aware Image Synthesis: Learning to Generate 3D Content from Images
Yao Yao - BlendedMVS: A Low-cost Image and Depth Synthesis Pipeline for Multi-view Geometry Learning
Lap-Fai (Craig) Yu and Sai-Kit Yeung - Understanding Real Scenes for Mixed Reality Applications
Examples of PIES
Examples of PIES
Matterport3D
Matterport3D
Chang, A., Dai, A., Funkhouser, T., Halber, M., Niessner, M., Savva, M., Song, S., Zeng, A. and Zhang, Y. 2017. Matterport3d: Learning from rgb-d data in indoor environments. arXiv preprint arXiv:1709.06158. (2017).
3D-FRONT
3D-FRONT
Fu, H., Cai, B., Gao, L., Zhang, L., Li, C., Xun, Z., Sun, C., Fei, Y., Zheng, Y. and Li, Y. 2020. 3D- FRONT: 3D Furnished Rooms with layOuts and semaNTics. arXiv preprint arXiv:2011.09127. (2020).
Replica Dataset
Replica Dataset
Straub, J., Whelan, T., Ma, L., Chen, Y., Wijmans, E., Green, S., Engel, J.J., Mur-Artal, R., Ren, C. and Verma, S. 2019. The Replica dataset: A digital replica of indoor spaces. arXiv preprint arXiv:1906.05797. (2019).
Structured3D
Structured3D
Zheng, J., Zhang, J., Li, J., Tang, R., Gao, S. and Zhou, Z. 2019. Structured3d: A large photo- realistic dataset for structured 3d modeling. arXiv preprint arXiv:1908.00222. 2, 7 (2019).
Questions?
Questions?
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