This four-year, $400,000 Project Grant from the National Science Foundation's Division of Information and Intelligent Systems supports research at the Massachusetts Institute of Technology under the Computer and Information Science and Engineering program.
The grant funds the development of new techniques for composing implicit representations to enable machines to perceive and reconstruct 3D scenes in a more generalizable manner. Investigators will explore methods to make representations...
This $300,000 federal Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) aims to enhance computer vision capabilities for perceiving and understanding shapes in images and 3D data. The key products and services to be delivered through this 3-year award include:
Developing deep learning models and techniques to enable computers to focus on the critical parts that constitute an object and recognize the...
This $600,000 Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) supports the development of a novel Bayesian diffusion model (BDM) framework to advance visual perception and cognition systems.
The project aims to revisit the "analysis-by-synthesis" methodology by carefully integrating generative models and Bayesian inference to enable enhanced computer vision applications, including 3D...
This National Science Foundation (NSF) Division of Information and Intelligent Systems (CFDA #47.070) $1,034,326 Project Grant awarded to Brown University supports collaborative research to bridge the semantic-metric gap in multinocular 3D vision systems. The project aims to address key technical challenges in 3D computer vision, such as handling blurry/textureless images, leveraging redundant image data, and connecting geometric point clouds to semantic scene representations. The research...
The National Science Foundation awarded a $500,000 Project Grant to the University of California, San Diego under the Computer and Information Science and Engineering federal grant program (CFDA 47.070) to support the "PANOPTIC 3D PARSING IN THE WILD" research project. The three-year award, which runs from October 1, 2021 through September 30, 2024, will fund the development of technologies to enable panoptic 3D scene parsing using computer vision techniques for unconstrained images...
This federal Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) provides $299,964 to Stony Brook University to enhance how computers understand and recognize shapes in images and 3D data. The project aims to teach computers to focus on the critical parts of an image that constitute an object, disregarding the background and other distractions, and to comprehend the relationships between different parts...
The National Science Foundation (NSF) has awarded a $600,000 five-year Project Grant under its Computer and Information Science and Engineering (CFDA 47.070) program to the University of California, San Diego (UCSD) to conduct research on "Interaction-Oriented 3D Representation Learning on Point Cloud". The research aims to develop novel deep learning frameworks that can learn 3D representations from point cloud data and leverage these representations to optimally plan and execute...
The National Science Foundation (NSF) awarded a $449,795 Project Grant under the Engineering program (CFDA 47.041) to the University of Pittsburgh. The 5-year award, effective May 1, 2024, aims to develop a bio-inspired sensing, computing, and learning framework for next-generation computer vision (CV) systems. The proposed research will focus on three key areas: 1) creating retina-inspired vision sensors that outperform existing cameras, 2) modeling and implementing scalable corticomorphic...
This four-year, $400,000 project grant from the National Science Foundation's Computer and Information Science and Engineering program aims to develop new techniques for 3D scene understanding. Specifically, the Stanford University researchers will explore implicit neural representations to model scene structure and details from raw images and videos. They will integrate findings into course development and partner with organizations to teach artificial intelligence, computer vision, and...
This $300,000 project grant from the National Science Foundation's Computer and Information Science and Engineering program (CFDA 47.070) supports research at New York University to develop efficient computer systems for augmented and virtual reality through a perception-guided approach. Specifically, the researchers will design software-hardware mechanisms to quantitatively model human visual perception and leverage those models across system stacks to achieve an order of magnitude gain in...