490 citations · 1k across the 74 of their papers we have counts for
6 papers · 1 filter
GenMatter: Perceiving Physical Objects with Generative Matter Models
Eric Li, Arijit Dasgupta, Yoni Friedman +5
Human visual perception offers valuable insights for understanding computational principles of motion-based scene interpretation. Humans robustly detect and segment moving entities…
3D Neural Embedding Likelihood: Probabilistic Inverse Graphics for Robust 6D Pose Estimation
Guangyao Zhou, Nishad Gothoskar, Lirui Wang +5
The ability to perceive and understand 3D scenes is crucial for many applications in computer vision and robotics. Inverse graphics is an appealing approach to 3D scene understandi…
ProbNeRF: Uncertainty-Aware Inference of 3D Shapes from 2D Images
Matthew D. Hoffman, Tuan Anh Le, Pavel Sountsov +4
The problem of inferring object shape from a single 2D image is underconstrained. Prior knowledge about what objects are plausible can help, but even given such prior knowledge the…
3DP3: 3D Scene Perception via Probabilistic Programming
Nishad Gothoskar, Marco Cusumano-Towner, Ben Zinberg +6
We present 3DP3, a framework for inverse graphics that uses inference in a structured generative model of objects, scenes, and images. 3DP3 uses (i) voxel models to represent the 3…
Real-time Approximate Bayesian Computation for Scene Understanding
Javier Felip, Nilesh Ahuja, David Gómez-Gutiérrez +2
Consider scene understanding problems such as predicting where a person is probably reaching, or inferring the pose of 3D objects from depth images, or inferring the probable stree…
Inverse Graphics with Probabilistic CAD Models
Tejas D. Kulkarni, Vikash K. Mansinghka, Pushmeet Kohli +1
Recently, multiple formulations of vision problems as probabilistic inversions of generative models based on computer graphics have been proposed. However, applications to 3D perce…