66 citations · 587 across the 53 of their papers we have counts for
66 papers
Flexible Techniques for Differentiable Rendering with 3D Gaussians
Leonid Keselman, Martial Hebert
Fast, reliable shape reconstruction is an essential ingredient in many computer vision applications. Neural Radiance Fields demonstrated that photorealistic novel view synthesis is…
Optimizing Algorithms From Pairwise User Preferences
Leonid Keselman, Katherine Shih, Martial Hebert +1
Typical black-box optimization approaches in robotics focus on learning from metric scores. However, that is not always possible, as not all developers have ground truth available.…
Thinking Like an Annotator: Generation of Dataset Labeling Instructions
Nadine Chang, Francesco Ferroni, Michael J. Tarr +2
Large-scale datasets are essential to modern day deep learning. Advocates argue that understanding these methods requires dataset transparency (e.g. "dataset curation, motivation,…
Object Discovery from Motion-Guided Tokens
Zhipeng Bao, Pavel Tokmakov, Yu-Xiong Wang +2
Object discovery -- separating objects from the background without manual labels -- is a fundamental open challenge in computer vision. Previous methods struggle to go beyond clust…
Discovering Multiple Algorithm Configurations
Leonid Keselman, Martial Hebert
Many practitioners in robotics regularly depend on classic, hand-designed algorithms. Often the performance of these algorithms is tuned across a dataset of annotated examples whic…
Deep Projective Rotation Estimation through Relative Supervision
Brian Okorn, Chuer Pan, Martial Hebert +1
Orientation estimation is the core to a variety of vision and robotics tasks such as camera and object pose estimation. Deep learning has offered a way to develop image-based orien…