Search Results for author: Davis Rempe

Found 8 papers, 5 papers with code

Generating Useful Accident-Prone Driving Scenarios via a Learned Traffic Prior

no code implementations9 Dec 2021 Davis Rempe, Jonah Philion, Leonidas J. Guibas, Sanja Fidler, Or Litany

Scenario generation is formulated as an optimization in the latent space of this traffic model, perturbing an initial real-world scene to produce trajectories that collide with a given planner.

Autonomous Vehicles

A Point-Cloud Deep Learning Framework for Prediction of Fluid Flow Fields on Irregular Geometries

no code implementations15 Oct 2020 Ali Kashefi, Davis Rempe, Leonidas J. Guibas

Grid vertices in a computational fluid dynamics (CFD) domain are viewed as point clouds and used as inputs to a neural network based on the PointNet architecture, which learns an end-to-end mapping between spatial positions and CFD quantities.

CaSPR: Learning Canonical Spatiotemporal Point Cloud Representations

1 code implementation NeurIPS 2020 Davis Rempe, Tolga Birdal, Yongheng Zhao, Zan Gojcic, Srinath Sridhar, Leonidas J. Guibas

We propose CaSPR, a method to learn object-centric Canonical Spatiotemporal Point Cloud Representations of dynamically moving or evolving objects.

Pose Estimation

Contact and Human Dynamics from Monocular Video

1 code implementation ECCV 2020 Davis Rempe, Leonidas J. Guibas, Aaron Hertzmann, Bryan Russell, Ruben Villegas, Jimei Yang

Existing deep models predict 2D and 3D kinematic poses from video that are approximately accurate, but contain visible errors that violate physical constraints, such as feet penetrating the ground and bodies leaning at extreme angles.

Human Dynamics Pose Estimation

Predicting the Physical Dynamics of Unseen 3D Objects

1 code implementation16 Jan 2020 Davis Rempe, Srinath Sridhar, He Wang, Leonidas J. Guibas

Experiments show that we can accurately predict the changes in state for unseen object geometries and initial conditions.

Multiview Aggregation for Learning Category-Specific Shape Reconstruction

1 code implementation NeurIPS 2019 Srinath Sridhar, Davis Rempe, Julien Valentin, Sofien Bouaziz, Leonidas J. Guibas

We investigate the problem of learning category-specific 3D shape reconstruction from a variable number of RGB views of previously unobserved object instances.

3D Shape Reconstruction

Learning Generalizable Physical Dynamics of 3D Rigid Objects

no code implementations2 Jan 2019 Davis Rempe, Srinath Sridhar, He Wang, Leonidas J. Guibas

In this work, we focus on predicting the dynamics of 3D rigid objects, in particular an object's final resting position and total rotation when subjected to an impulsive force.

Autonomous Vehicles

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