Guest
Shubham Tulsiani Assistant Professor Robotics Institute, Carnegie Mellon University October 22, 2021 Towards Reconstructing Any Object in 3D Abstract: The world we live in is incredibly diverse, comprising of over 10k natural and man-made object categories. While the computer vision community has made impressive progress in classifying images from such diverse categories, the state-of-the-art 3D prediction systems are still limited to merely tens of object classes. A key reason for this stark difference is the relative difficulty of acquiring supervision — while it is easy to annotate a semantic label for an image, obtaining ground-truth 3D for learning at scale is infeasible. But do we really need such ground-truth 3D for learning? In this talk, I will present a learning-based approach that can train from unstructured image collections, using only segmentation outputs from off-the-shelf recognition syst
