4 citations · 7 across the 4 of their papers we have counts for
6 papers
SMORE: Simultaneous Map and Object REconstruction
Nathaniel Chodosh, Anish Madan, Simon Lucey +1
We present a method for dynamic surface reconstruction of large-scale urban scenes from LiDAR. Depth-based reconstructions tend to focus on small-scale objects or large-scale SLAM…
ZeroFlow: Scalable Scene Flow via Distillation
Kyle Vedder, Neehar Peri, Nathaniel Chodosh +6
Scene flow estimation is the task of describing the 3D motion field between temporally successive point clouds. State-of-the-art methods use strong priors and test-time optimizatio…
Re-Evaluating LiDAR Scene Flow for Autonomous Driving
Nathaniel Chodosh, Deva Ramanan, Simon Lucey
Popular benchmarks for self-supervised LiDAR scene flow (stereoKITTI, and FlyingThings3D) have unrealistic rates of dynamic motion, unrealistic correspondences, and unrealistic sam…
When to Use Convolutional Neural Networks for Inverse Problems
Nathaniel Chodosh, Simon Lucey
Reconstruction tasks in computer vision aim fundamentally to recover an undetermined signal from a set of noisy measurements. Examples include super-resolution, image denoising, an…
DeepGeo: Photo Localization with Deep Neural Network
Sudharshan Suresh, Nathaniel Chodosh, Montiel Abello
In this paper we address the task of determining the geographical location of an image, a pertinent problem in learning and computer vision. This research was inspired from playing…
Deep Convolutional Compressed Sensing for LiDAR Depth Completion
Nathaniel Chodosh, Chaoyang Wang, Simon Lucey
In this paper we consider the problem of estimating a dense depth map from a set of sparse LiDAR points. We use techniques from compressed sensing and the recently developed Altern…