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Namdar Homayounfar

7 accepted papers

2020

PolyTransform: Deep Polygon Transformer for Instance Segmentation

CVPR 2020poster

In this paper, we propose PolyTransform, a novel instance segmentation algorithm that produces precise, geometry-preserving masks by combining the strengths of prevailing segmentation approaches and modern polygon-based methods. In particular, we first exploit a segmentation network to generate inst…

Cited by 218PDFScholar
2019

Convolutional Recurrent Network for Road Boundary Extraction

CVPR 2019poster

Creating high definition maps that contain precise information of static elements of the scene is of utmost importance for enabling self driving cars to drive safely. In this paper, we tackle the problem of drivable road boundary extraction from LiDAR and camera imagery. Towards this goal, we design…

Cited by 86PDFScholar
2019

DAGMapper: Learning to Map by Discovering Lane Topology

ICCV 2019poster

One of the fundamental challenges to scale self-driving is being able to create accurate high definition maps (HD maps) with low cost. Current attempts to automate this pro- cess typically focus on simple scenarios, estimate independent maps per frame or do not have the level of precision required b…

Cited by 133PDFScholar
2019

Exploiting Sparse Semantic HD Maps for Self-Driving Vehicle Localization

IROS 2019poster

In this paper we propose a novel semantic localization algorithm that exploits multiple sensors and has precision on the order of a few centimeters. Our approach does not require detailed knowledge about the appearance of the world, and our maps require orders of magnitude less storage than maps uti…

Cited by 147SourceScholar
2018

Deep Multi-Sensor Lane Detection

IROS 2018poster

Reliable and accurate lane detection has been a long-standing problem in the field of autonomous driving. In recent years, many approaches have been developed that use images (or videos) as input and reason in image space. In this paper we argue that accurate image estimates do not translate to prec…

Cited by 108SourceScholar
2018

Hierarchical Recurrent Attention Networks for Structured Online Maps

CVPR 2018poster

In this paper, we tackle the problem of online road network extraction from sparse 3D point clouds. Our method is inspired by how an annotator builds a lane graph, by first identifying how many lanes there are and then drawing each one in turn. We develop a hierarchical recurrent network that atten…

Cited by 77SourcePDFScholar