We are an inter-disciplinary team of researchers working in visual computing, in particular, computer graphics and computer vision. Current areas of focus include 3D and robotic vision, 3D printing and content creation, animation, AR/VR, geometric and image-based modelling, machine learning, natural phenomenon, and shape analysis. Our research works frequently appear in top venues such as SIGGRAPH, CVPR, and ICCV (we rank #11 in the world in terms of top publications in visual computing, as of 7/2020) and we collaborate widely with the industry and academia (e.g., Adobe Research, Google, MSRA, Princeton, Stanford, and Washington). Our faculty and students have won numerous honours and awards, including FRSC, Alain Fournier Best Thesis Award, Google Faculty Award, TR35@Singapore, NSERC Discovery Accelerator, and several best paper awards from ECCV, SCA, SGP, etc. Gruvi alumni went on to take up faculty positions in Canada, the US, and Asia, while others now work at companies including Apple, EA, Facebook, Google, IBM, and Microsoft.
July 8, 2020
Congratulations to Angel Xuan Chang and Manolis Savva, whose work ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes won the 2020 SGP dataset award. ScanNet is an RGB-D video dataset containing 2.5 million views in more than 1500 scans, annotated with 3D camera poses, surface reconstructions, and instance-level semantic segmentations. More information can be found in the paper here.
Jun 23, 2020
Visual computing researchers from SFU received multiple awards at the annual Conference on Computer Vision and Pattern Recognition (CVPR) this past week. CVPR is the premier conference in computer vision with the highest impact factor among all conferences in computer science and was held virtually for the first time this year from June 14-19. Computing science professor Greg Mori served as one of four program chairs at the conference. Zhiqin Chen and Richard Zhang, together with GrUVi alumnus Andrea Tagliasacchi, won the Best Student Paper Award with “BSP-Net: Generating Compact Meshes via Binary Space Partitioning” introduces a deep neural network which applies a classical graphics technique to learn compact shape representations. Yasutaka Furukawa won the PAMI Longuet-Higgins Prize for his CVPR 2007 milestone paper on “multi-view stereo reconstruction”, which has been cited more than 3,000 times! Also, Akshay Gadi Patil won the Best Paper Award at the CVPR Workshop on Text and Documents in the Deep Learning Era for his work “READ: Recursive Autoencoders for Document Layout Generation”. Read more here. Well done, gruviers!
Jun 09, 2020
CVPR, the premier conference on computer vision, will be held virtually next week (June 16-20). SFU Computing Science professor Greg Mori is front-n-center as a Program Chair of the major event! GrUVi lab will have an incredible showing at CVPR, with 11 technical papers (5 orals), 3 invited talks, and 4 co-organized workshops! Workshop co-organization GrUViers will co-organize 4 workshops featuring state-of-the-art research: Learning 3D Generative Models ScanNet Indoor Scene Understanding Challenge Embodied-AI Workshop Deep Learning Foundations of Geometric Shape Modeling and Reconstruction Invited workshop talks Yasutaka Furukawa will give a talk at the aforementioned “ScanNet Indoor Scene Understanding Challenge” as well as the “3D Scene Understanding for Vision, Graphics, and Robotics” workshop , while Manolis Savva will participate as an invited speaker at the “Fair, Data, Efficient and Trusted Computer Vision” workshop. Technical Papers and GrUVi authors Congratulations to all authors, especially to Dr. Ping Tan, who got five accepted papers! The full list of papers featured on CVPR 2020 can be accessed here. In particular, GrUVi papers covered different topics: 3D From a Single Image and Shape-From-X BSP-Net: Generating Compact Meshes via Binary Space Partitioning (Zhiqin Chen, Andrea Tagliasacchi, Hao Zhang - oral) Bundle Pooling for Polygonal Architecture Segmentation Problem (Huayi Zeng, Kevin Joseph, Adam Vest, Yasutaka Furukawa) Cascade Cost Volume for High-Resolution Multi-View Stereo and Stereo Matching (Xiaodong Gu, Zhiwen Fan, Siyu Zhu, Zuozhuo Dai, Feitong Tan, Ping Tan - oral) Conv-MPN: Convolutional Message Passing Neural Network for Structured Outdoor Architecture Reconstruction (Nelson Nauata, Fuyang Zhang, Yasutaka Furukawa) PQ-NET: A Generative Part Seq2Seq Network for 3D Shapes (Rundi Wu, Yixin Zhuang, Kai Xu, Hao Zhang, Baoquan Chen) Self-Supervised Human Depth Estimation From Monocular Videos (Feitong Tan, Hao Zhu, Zhaopeng Cui, Siyu Zhu, Marc Pollefeys, Ping Tan) 3D From Multiview and Sensors; Computational Photography; Efficient Training and Inference Methods for Networks End-to-End Learning Local Multi-View Descriptors for 3D Point Clouds (Lei Li, Siyu Zhu, Hongbo Fu, Ping Tan, Chiew-Lan Tai) 3D From Multiview and Sensors; Face, Gesture, and Body Pose; Image and Video Synthesis Deep Facial Non-Rigid Multi-View Stereo (Ziqian Bai, Zhaopeng Cui, Jamal Ahmed Rahim, Xiaoming Liu, Ping Tan) Motion and Tracking LSM: Learning Subspace Minimization for Low-Level Vision (Chengzhou Tang, Lu Yuan, Ping Tan - oral) Segmentation, Grouping, and Shape AdaCoSeg: Adaptive Shape Co-Segmentation With Group Consistency Loss (Chenyang Zhu, Kai Xu, Siddhartha Chaudhuri, Li Yi, Leonidas J. Guibas, Hao Zhang - oral) Vision for Robotics and Autonomous Vehicles SAPIEN: A SimulAted Part-Based Interactive ENvironment (Fanbo Xiang, Yuzhe Qin, Kaichun Mo, Yikuan Xia, Hao Zhu, Fangchen Liu, Minghua Liu, Hanxiao Jiang, Yifu Yuan, He Wang, Li Yi, Angel X. Chang, Leonidas J. Guibas, Hao Su - oral)
Jun 07, 2020
SFU researchers helped to develop an AI system capable of assisting resident and less experienced doctors look over a data set and make a quick diagnosis before a senior doctor can step in. This is accoding to Yağız Aksoy, a gruvier and also a member of the team that proposed the diagnosis tool, which is currently in the validation phase at St. Paul’s Hospital in Vancouver, Canada. Read more about it here. Thank you for your hard work, Dr. Aksoy!