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.
Note: PIES-CV will be held as a hybrid workshop.
Nicholas R. Gans, University of Texas at Arlington
Ryan P. McMahan, University of Central Florida
List of Invited Speakers
Angel Chang - Creating Realistic and Interactive 3D Environments
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
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).
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).
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).
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).