activity
20172020
most citedEfficient Hierarchical Graph-Based Segmentation of RGBD Videos

52 citations · 87 across the 7 of their papers we have counts for

collaborators

7 papers

cs.RO2020

Indirect Object-to-Robot Pose Estimation from an External Monocular RGB Camera

Jonathan Tremblay, Stephen Tyree, Terry Mosier +1

We present a robotic grasping system that uses a single external monocular RGB camera as input. The object-to-robot pose is computed indirectly by combining the output of two neura…

cs.CV20207 cited

Improving Deep Stereo Network Generalization with Geometric Priors

Jialiang Wang, Varun Jampani, Deqing Sun +3

End-to-end deep learning methods have advanced stereo vision in recent years and obtained excellent results when the training and test data are similar. However, large datasets of…

cs.RO20205 cited

RMPflow: A Geometric Framework for Generation of Multi-Task Motion Policies

Ching-An Cheng, Mustafa Mukadam, Jan Issac +4

Generating robot motion for multiple tasks in dynamic environments is challenging, requiring an algorithm to respond reactively while accounting for complex nonlinear relationships…

cs.CV20207 cited

MVLidarNet: Real-Time Multi-Class Scene Understanding for Autonomous Driving Using Multiple Views

Ke Chen, Ryan Oldja, Nikolai Smolyanskiy +5

Autonomous driving requires the inference of actionable information such as detecting and classifying objects, and determining the drivable space. To this end, we present Multi-Vie…

cs.CV2020

PAMTRI: Pose-Aware Multi-Task Learning for Vehicle Re-Identification Using Highly Randomized Synthetic Data

Zheng Tang, Milind Naphade, Stan Birchfield +5

In comparison with person re-identification (ReID), which has been widely studied in the research community, vehicle ReID has received less attention. Vehicle ReID is challenging d…

cs.CV201852 cited

Efficient Hierarchical Graph-Based Segmentation of RGBD Videos

Steven Hickson, Stan Birchfield, Irfan Essa +1

We present an efficient and scalable algorithm for segmenting 3D RGBD point clouds by combining depth, color, and temporal information using a multistage, hierarchical graph-based…