activity
20162022
most citedGlobal-Local Face Upsampling Network

21 citations · 38 across the 8 of their papers we have counts for

collaborators

8 papers

cs.RO2022★ 6 cited

PlaneSDF-based Change Detection for Long-term Dense Mapping

Jiahui Fu, Chengyuan Lin, Yuichi Taguchi +4

The ability to process environment maps across multiple sessions is critical for robots operating over extended periods of time. Specifically, it is desirable for autonomous agents…

cs.CV2019

Unified Underwater Structure-from-Motion

Kazuto Ichimaru, Yuichi Taguchi, Hiroshi Kawasaki

This paper shows that accurate underwater 3D shape reconstruction is possible using a single camera, observing a target through a refractive interface. We provide unified reconstru…

cs.CV2018★ 1 cited

VLASE: Vehicle Localization by Aggregating Semantic Edges

Xin Yu, Sagar Chaturvedi, Chen Feng +4

In this paper, we propose VLASE, a framework to use semantic edge features from images to achieve on-road localization. Semantic edge features denote edge contours that separate pa…

cs.CV2018★ 3 cited

Joint 3D Reconstruction of a Static Scene and Moving Objects

Sergio Caccamo, Esra Ataer-Cansizoglu, Yuichi Taguchi

We present a technique for simultaneous 3D reconstruction of static regions and rigidly moving objects in a scene. An RGB-D frame is represented as a collection of features, which…

cs.CV2017★ 7 cited

3D Object Discovery and Modeling Using Single RGB-D Images Containing Multiple Object Instances

Wim Abbeloos, Esra Ataer-Cansizoglu, Sergio Caccamo +2

Unsupervised object modeling is important in robotics, especially for handling a large set of objects. We present a method for unsupervised 3D object discovery, reconstruction, and…

cs.CV2017

Detecting and Grouping Identical Objects for Region Proposal and Classification

Wim Abbeloos, Sergio Caccamo, Esra Ataer-Cansizoglu +3

Often multiple instances of an object occur in the same scene, for example in a warehouse. Unsupervised multi-instance object discovery algorithms are able to detect and identify s…