Multi-view capture systems are complex systems to engineer. They require technical knowledge to install and intricate processes to setup related mainly to the sensors' spatial alignment (i.e. external calibration). However, with the ongoing developments in new production methods, we are now at a position where the production of high quality realistic 3D assets is possible even with commodity sensors. Nonetheless, the capturing systems developed with these methods are heavily intertwined with the methods themselves, relying on custom solutions and seldom - if not at all - publicly available. In light of this, we design, develop and publicly offer a multi-view capture system based on the latest RGB-D sensor technology. For our system, we develop a portable and easy-to-use external calibration method that greatly reduces the effort and knowledge required, as well as simplify the overall process.
Enhanced Portable Ultra Low-Field Diffusion Tensor Imaging with Bayesian Artifact Correction and Deep Learning-Based Super-Resolution
Picower Institute, Massachusetts Institute of Technology（麻省理工学院皮考尔研究所）；Institute for Medical Engineering and Science, Massachusetts Institute of Technology（麻省理工学院医学工程与科学研究所）；Massachusetts General Hospital（麻省总医院）；Harvard Medical School（哈佛医学院）；MIT/MGH Brain Arousal State Control Innovation Center, Massachusetts Institute of Technology（麻省理工学院MIT/MGH脑觉醒状态控制创新中心）；Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School（麻省总医院与哈佛医学院阿西诺拉·A·马蒂诺斯生物医学成像中心）；Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology（麻省理工学院计算机科学与人工智能实验室）；Hawkes Institute, University College London（伦敦大学学院霍克斯研究所）
Ground Reaction Force Estimation via Time-aware Knowledge Distillation
Department of Computer Science and Engineering, Seoul National University of Science and Technology（首尔科学技术大学计算机科学与工程系）；Geometric Media Lab, School of Arts, Media and Engineering and School of Electrical, Computer and Energy Engineering, Arizona State University（亚利桑那州立大学几何媒体实验室、艺术、媒体与工程学院和电气、计算机与能源工程学院）；School for Engineering of Matter, Transport and Energy, Arizona State University（亚利桑那州立大学工程科学学院）；Department of Computer Science and Engineering, University of Nevada, Reno（内华达大学里诺分校计算机科学与工程系）
T3D: Advancing 3D Medical Vision-Language Pre-training by Learning Multi-View Visual Consistency
Imperial College London（帝国理工学院）；University of Oxford（牛津大学）；Hong Kong University of Science and Technology（香港科技大学）；Peking University（北京大学）；University of Science and Technology of China（中国科学技术大学）
