15 citations · 16 across the 6 of their papers we have counts for
7 papers
Geodesic-HOF: 3D Reconstruction Without Cutting Corners
Ziyun Wang, Eric A. Mitchell, Volkan Isler +1
Single-view 3D object reconstruction is a challenging fundamental problem in computer vision, largely due to the morphological diversity of objects in the natural world. In particu…
Near-chip Dynamic Vision Filtering for Low-Bandwidth Pedestrian Detection
Anthony Bisulco, Fernando Cladera Ojeda, Volkan Isler +1
This paper presents a novel end-to-end system for pedestrian detection using Dynamic Vision Sensors (DVSs). We target applications where multiple sensors transmit data to a local p…
Surface HOF: Surface Reconstruction from a Single Image Using Higher Order Function Networks
Ziyun Wang, Volkan Isler, Daniel D. Lee
We address the problem of generating a high-resolution surface reconstruction from a single image. Our approach is to learn a Higher Order Function (HOF) which takes an image of an…
Higher Order Function Networks for View Planning and Multi-View Reconstruction
Selim Engin, Eric Mitchell, Daewon Lee +2
We consider the problem of planning views for a robot to acquire images of an object for visual inspection and reconstruction. In contrast to offline methods which require a 3D mod…
Higher-Order Function Networks for Learning Composable 3D Object Representations
Eric Mitchell, Selim Engin, Volkan Isler +1
We present a new approach to 3D object representation where a neural network encodes the geometry of an object directly into the weights and biases of a second 'mapping' network. T…
Pixels to Plans: Learning Non-Prehensile Manipulation by Imitating a Planner
Tarik Tosun, Eric Mitchell, Ben Eisner +6
We present a novel method enabling robots to quickly learn to manipulate objects by leveraging a motion planner to generate "expert" training trajectories from a small amount of hu…