Search Results for author: Sudeep Pillai

Found 14 papers, 5 papers with code

Neural Ray Surfaces for Self-Supervised Learning of Depth and Ego-motion

1 code implementation15 Aug 2020 Igor Vasiljevic, Vitor Guizilini, Rares Ambrus, Sudeep Pillai, Wolfram Burgard, Greg Shakhnarovich, Adrien Gaidon

Self-supervised learning has emerged as a powerful tool for depth and ego-motion estimation, leading to state-of-the-art results on benchmark datasets.

Depth Estimation Motion Estimation +2

PillarFlow: End-to-end Birds-eye-view Flow Estimation for Autonomous Driving

no code implementations3 Aug 2020 Kuan-Hui Lee, Matthew Kliemann, Adrien Gaidon, Jie Li, Chao Fang, Sudeep Pillai, Wolfram Burgard

In autonomous driving, accurately estimating the state of surrounding obstacles is critical for safe and robust path planning.

Autonomous Driving

Neural Outlier Rejection for Self-Supervised Keypoint Learning

2 code implementations ICLR 2020 Jiexiong Tang, Hanme Kim, Vitor Guizilini, Sudeep Pillai, Rares Ambrus

By making the sampling of inlier-outlier sets from point-pair correspondences fully differentiable within the keypoint learning framework, we show that are able to simultaneously self-supervise keypoint description and improve keypoint matching.

Homography Estimation Keypoint Detection +1

Robust Semi-Supervised Monocular Depth Estimation with Reprojected Distances

no code implementations4 Oct 2019 Vitor Guizilini, Jie Li, Rares Ambrus, Sudeep Pillai, Adrien Gaidon

Dense depth estimation from a single image is a key problem in computer vision, with exciting applications in a multitude of robotic tasks.

Monocular Depth Estimation

Two Stream Networks for Self-Supervised Ego-Motion Estimation

no code implementations4 Oct 2019 Rares Ambrus, Vitor Guizilini, Jie Li, Sudeep Pillai, Adrien Gaidon

Learning depth and camera ego-motion from raw unlabeled RGB video streams is seeing exciting progress through self-supervision from strong geometric cues.

Data Augmentation Motion Estimation +1

Self-Supervised Visual Place Recognition Learning in Mobile Robots

no code implementations11 May 2019 Sudeep Pillai, John Leonard

Place recognition is a critical component in robot navigation that enables it to re-establish previously visited locations, and simultaneously use this information to correct the drift incurred in its dead-reckoned estimate.

Metric Learning Robot Navigation +1

SuperDepth: Self-Supervised, Super-Resolved Monocular Depth Estimation

no code implementations3 Oct 2018 Sudeep Pillai, Rares Ambrus, Adrien Gaidon

Both contributions provide significant performance gains over the state-of-the-art in self-supervised depth and pose estimation on the public KITTI benchmark.

Depth Prediction Image Super-Resolution +3

Towards Visual Ego-motion Learning in Robots

no code implementations29 May 2017 Sudeep Pillai, John J. Leonard

Many model-based Visual Odometry (VO) algorithms have been proposed in the past decade, often restricted to the type of camera optics, or the underlying motion manifold observed.

Motion Estimation Optical Flow Estimation +2

High-Performance and Tunable Stereo Reconstruction

no code implementations3 Nov 2015 Sudeep Pillai, Srikumar Ramalingam, John J. Leonard

Traditional stereo algorithms have focused their efforts on reconstruction quality and have largely avoided prioritizing for run time performance.

Disparity Estimation Stereo Disparity Estimation

Monocular SLAM Supported Object Recognition

no code implementations4 Jun 2015 Sudeep Pillai, John Leonard

In this work, we develop a monocular SLAM-aware object recognition system that is able to achieve considerably stronger recognition performance, as compared to classical object recognition systems that function on a frame-by-frame basis.

Object Recognition

Learning Articulated Motions From Visual Demonstration

1 code implementation5 Feb 2015 Sudeep Pillai, Matthew R. Walter, Seth Teller

This paper describes a method by which a robot can acquire an object model by capturing depth imagery of the object as a human moves it through its range of motion.

Motion Segmentation Pose Estimation

Cannot find the paper you are looking for? You can Submit a new open access paper.