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researcher

Daniel D. Lee

12 papers hereh-index 12343 citations22 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • middle author1
  • last author10

Across the 11 of 12 papers where every author was matched, so the position is known.

fields
  • cs.RO6
  • cs.CV3
  • cs.LG2
  • cs.AI1
same name
  • Daniel D. Lee — 11 papers, h 12
  • Daniel D. Lee — 5 papers, h 15
  • Daniel D. Lee — 5 papers, h 30
  • Daniel D. Lee — 3 papers, h 2
  • Daniel D. Lee — 2 papers
  • Daniel D. Lee — 1 paper

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20192023
most citedEV-Catcher: High-Speed Object Catching Using Low-latency Event-based Neural Networks

26 citations · 43 across the 8 of their papers we have counts for

collaborators
Showing 2020Show all

4 papers · 1 filter

cs.RO2020

Learning to Generate Cost-to-Go Functions for Efficient Motion Planning

Jinwook Huh, Galen Xing, Ziyun Wang +2

Traditional motion planning is computationally burdensome for practical robots, involving extensive collision checking and considerable iterative propagation of cost values. We pre…

cs.CV2020★ 1 cited

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…

cs.CV2020

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…

cs.RO2020

Robotic Grasping through Combined Image-Based Grasp Proposal and 3D Reconstruction

Daniel Yang, Tarik Tosun, Ben Eisner +2

We present a novel approach to robotic grasp planning using both a learned grasp proposal network and a learned 3D shape reconstruction network. Our system generates 6-DOF grasps f…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.