Search Results for author: Namdar Homayounfar

Found 10 papers, 0 papers with code

VideoClick: Video Object Segmentation with a Single Click

no code implementations16 Jan 2021 Namdar Homayounfar, Justin Liang, Wei-Chiu Ma, Raquel Urtasun

Towards this goal, in this paper we propose a bottom up approach where given a single click for each object in a video, we obtain the segmentation masks of these objects in the full video.

Object Segmentation +4

DAGMapper: Learning to Map by Discovering Lane Topology

no code implementations ICCV 2019 Namdar Homayounfar, Wei-Chiu Ma, Justin Liang, Xinyu Wu, Jack Fan, Raquel Urtasun

One of the fundamental challenges to scale self-driving is being able to create accurate high definition maps (HD maps) with low cost.

Convolutional Recurrent Network for Road Boundary Extraction

no code implementations CVPR 2019 Justin Liang, Namdar Homayounfar, Wei-Chiu Ma, Shenlong Wang, Raquel Urtasun

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.

Self-Driving Cars

LevelSet R-CNN: A Deep Variational Method for Instance Segmentation

no code implementations30 Jul 2020 Namdar Homayounfar, Yuwen Xiong, Justin Liang, Wei-Chiu Ma, Raquel Urtasun

Obtaining precise instance segmentation masks is of high importance in many modern applications such as robotic manipulation and autonomous driving.

Autonomous Driving Instance Segmentation +2

PolyTransform: Deep Polygon Transformer for Instance Segmentation

no code implementations CVPR 2020 Justin Liang, Namdar Homayounfar, Wei-Chiu Ma, Yuwen Xiong, Rui Hu, Raquel Urtasun

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.

Ranked #1000000000 on Instance Segmentation on Cityscapes test (using extra training data)

Instance Segmentation Segmentation +1

Sports Field Localization via Deep Structured Models

no code implementations CVPR 2017 Namdar Homayounfar, Sanja Fidler, Raquel Urtasun

In this work, we propose a novel way of efficiently localizing a sports field from a single broadcast image of the game.

Semantic Segmentation

Soccer Field Localization from a Single Image

no code implementations10 Apr 2016 Namdar Homayounfar, Sanja Fidler, Raquel Urtasun

In this work, we propose a novel way of efficiently localizing a soccer field from a single broadcast image of the game.

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