55 citations · 133 across the 7 of their papers we have counts for
6 papers · 1 filter
SAM 3D: 3Dfy Anything in Images
SAM 3D Team, Xingyu Chen, Fu-Jen Chu +20
We present SAM 3D, a generative model for visually grounded 3D object reconstruction, predicting geometry, texture, and layout from a single image. SAM 3D excels in natural images,…
Omnidata: A Scalable Pipeline for Making Multi-Task Mid-Level Vision Datasets from 3D Scans
Ainaz Eftekhar, Alexander Sax, Roman Bachmann +2
This paper introduces a pipeline to parametrically sample and render multi-task vision datasets from comprehensive 3D scans from the real world. Changing the sampling parameters al…
Robust Learning Through Cross-Task Consistency
Amir Zamir, Alexander Sax, Teresa Yeo +6
Visual perception entails solving a wide set of tasks, e.g., object detection, depth estimation, etc. The predictions made for multiple tasks from the same image are not independen…
Learning to Navigate Using Mid-Level Visual Priors
Alexander Sax, Jeffrey O. Zhang, Bradley Emi +4
How much does having visual priors about the world (e.g. the fact that the world is 3D) assist in learning to perform downstream motor tasks (e.g. navigating a complex environment)…
Mid-Level Visual Representations Improve Generalization and Sample Efficiency for Learning Visuomotor Policies
Alexander Sax, Bradley Emi, Amir R. Zamir +3
How much does having visual priors about the world (e.g. the fact that the world is 3D) assist in learning to perform downstream motor tasks (e.g. delivering a package)? We study t…
Taskonomy: Disentangling Task Transfer Learning
Amir Zamir, Alexander Sax, William Shen +3
Do visual tasks have a relationship, or are they unrelated? For instance, could having surface normals simplify estimating the depth of an image? Intuition answers these questions…