Search Results for author: Rares Ambrus

Found 20 papers, 7 papers with code

Multi-Frame Self-Supervised Depth with Transformers

no code implementations15 Apr 2022 Vitor Guizilini, Rares Ambrus, Dian Chen, Sergey Zakharov, Adrien Gaidon

Experiments on the KITTI and DDAD datasets show that our DepthFormer architecture establishes a new state of the art in self-supervised monocular depth estimation, and is even competitive with highly specialized supervised single-frame architectures.

Frame Monocular Depth Estimation

Learning Optical Flow, Depth, and Scene Flow without Real-World Labels

no code implementations28 Mar 2022 Vitor Guizilini, Kuan-Hui Lee, Rares Ambrus, Adrien Gaidon

However, the simultaneous self-supervised learning of depth and scene flow is ill-posed, as there are infinitely many combinations that result in the same 3D point.

Autonomous Driving Monocular Depth Estimation +3

Self-Supervised Camera Self-Calibration from Video

no code implementations6 Dec 2021 Jiading Fang, Igor Vasiljevic, Vitor Guizilini, Rares Ambrus, Greg Shakhnarovich, Adrien Gaidon, Matthew R. Walter

Camera calibration is integral to robotics and computer vision algorithms that seek to infer geometric properties of the scene from visual input streams.

Autonomous Vehicles Camera Calibration +3

Is Pseudo-Lidar needed for Monocular 3D Object detection?

1 code implementation ICCV 2021 Dennis Park, Rares Ambrus, Vitor Guizilini, Jie Li, Adrien Gaidon

Recent progress in 3D object detection from single images leverages monocular depth estimation as a way to produce 3D pointclouds, turning cameras into pseudo-lidar sensors.

 Ranked #1 on Monocular 3D Object Detection on KITTI Pedestrian Hard (using extra training data)

Monocular 3D Object Detection Monocular Depth Estimation +1

Full Surround Monodepth from Multiple Cameras

no code implementations31 Mar 2021 Vitor Guizilini, Igor Vasiljevic, Rares Ambrus, Greg Shakhnarovich, Adrien Gaidon

In this work, we extend monocular self-supervised depth and ego-motion estimation to large-baseline multi-camera rigs.

Autonomous Driving Motion Estimation

Geometric Unsupervised Domain Adaptation for Semantic Segmentation

no code implementations ICCV 2021 Vitor Guizilini, Jie Li, Rares Ambrus, Adrien Gaidon

Simulators can efficiently generate large amounts of labeled synthetic data with perfect supervision for hard-to-label tasks like semantic segmentation.

Monocular Depth Estimation Semantic Segmentation +1

Monocular Depth Estimation for Soft Visuotactile Sensors

no code implementations5 Jan 2021 Rares Ambrus, Vitor Guizilini, Naveen Kuppuswamy, Andrew Beaulieu, Adrien Gaidon, Alex Alspach

Fluid-filled soft visuotactile sensors such as the Soft-bubbles alleviate key challenges for robust manipulation, as they enable reliable grasps along with the ability to obtain high-resolution sensory feedback on contact geometry and forces.

Monocular Depth Estimation

Probabilistic 3D Multi-Modal, Multi-Object Tracking for Autonomous Driving

no code implementations26 Dec 2020 Hsu-kuang Chiu, Jie Li, Rares Ambrus, Jeannette Bohg

Second, we propose to learn a metric that combines the Mahalanobis and feature distances when comparing a track and a new detection in data association.

Autonomous Driving Multi-Object Tracking +1

Driving Through Ghosts: Behavioral Cloning with False Positives

no code implementations29 Aug 2020 Andreas Bühler, Adrien Gaidon, Andrei Cramariuc, Rares Ambrus, Guy Rosman, Wolfram Burgard

In this work, we propose a behavioral cloning approach that can safely leverage imperfect perception without being conservative.

Autonomous Driving

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

Semantically-Guided Representation Learning for Self-Supervised Monocular Depth

1 code implementation ICLR 2020 Vitor Guizilini, Rui Hou, Jie Li, Rares Ambrus, Adrien Gaidon

Instead of using semantic labels and proxy losses in a multi-task approach, we propose a new architecture leveraging fixed pretrained semantic segmentation networks to guide self-supervised representation learning via pixel-adaptive convolutions.

Monocular Depth Estimation Representation Learning +2

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

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.

Image Super-Resolution Monocular Depth Estimation +1

Unsupervised Object Discovery and Segmentation of RGBD-images

no code implementations18 Oct 2017 Johan Ekekrantz, Nils Bore, Rares Ambrus, John Folkesson, Patric Jensfelt

In this paper we introduce a system for unsupervised object discovery and segmentation of RGBD-images.

Object Discovery

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