activity
20162021
most citedDeep Level Sets: Implicit Surface Representations for 3D Shape Inference

60 citations · 65 across the 5 of their papers we have counts for

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Showing 2018Show all

5 papers · 1 filter

cs.CV2018

Star Tracking using an Event Camera

Tat-Jun Chin, Samya Bagchi, Anders Eriksson +1

Star trackers are primarily optical devices that are used to estimate the attitude of a spacecraft by recognising and tracking star patterns. Currently, most star trackers use conv…

cs.CV2018

A Binary Optimization Approach for Constrained K-Means Clustering

Huu Le, Anders Eriksson, Thanh-Toan Do +1

K-Means clustering still plays an important role in many computer vision problems. While the conventional Lloyd method, which alternates between centroid update and cluster assignm…

cs.RO2018

3D Move to See: Multi-perspective visual servoing for improving object views with semantic segmentation

Chris Lehnert, Dorian Tsai, Anders Eriksson +1

In this paper, we present a new approach to visual servoing for robotics, referred to as 3D Move to See (3DMTS), based on the principle of finding the next best view using a 3D cam…

cs.CV2018

Learning Free-Form Deformations for 3D Object Reconstruction

Dominic Jack, Jhony K. Pontes, Sridha Sridharan +4

Representing 3D shape in deep learning frameworks in an accurate, efficient and compact manner still remains an open challenge. Most existing work addresses this issue by employing…

cs.CV2018

Maximum Consensus Parameter Estimation by Reweighted Methods

Pulak Purkait, Christopher Zach, Anders Eriksson

Robust parameter estimation in computer vision is frequently accomplished by solving the maximum consensus (MaxCon) problem. Widely used randomized methods for MaxCon, however, can…