Search Results for author: Rares Ambrus

Found 39 papers, 13 papers with code

Zero-Shot Multi-Object Shape Completion

no code implementations21 Mar 2024 Shun Iwase, Katherine Liu, Vitor Guizilini, Adrien Gaidon, Kris Kitani, Rares Ambrus, Sergey Zakharov

We present a 3D shape completion method that recovers the complete geometry of multiple objects in complex scenes from a single RGB-D image.

Object

Understanding Video Transformers via Universal Concept Discovery

no code implementations19 Jan 2024 Matthew Kowal, Achal Dave, Rares Ambrus, Adrien Gaidon, Konstantinos G. Derpanis, Pavel Tokmakov

To this end, we propose an efficient approach for unsupervised identification of units of video transformer representations - concepts, and ranking their importance to the output of a model.

Decision Making

FSD: Fast Self-Supervised Single RGB-D to Categorical 3D Objects

no code implementations19 Oct 2023 Mayank Lunayach, Sergey Zakharov, Dian Chen, Rares Ambrus, Zsolt Kira, Muhammad Zubair Irshad

In this work, we address the challenging task of 3D object recognition without the reliance on real-world 3D labeled data.

3D Object Recognition 6D Pose Estimation

ShaSTA-Fuse: Camera-LiDAR Sensor Fusion to Model Shape and Spatio-Temporal Affinities for 3D Multi-Object Tracking

no code implementations4 Oct 2023 Tara Sadjadpour, Rares Ambrus, Jeannette Bohg

Our main contributions include a novel fusion approach for combining camera and LiDAR sensory signals to learn affinities, and a first-of-its-kind multimodal sequential track confidence refinement technique that fuses 2D and 3D detections.

3D Multi-Object Tracking Navigate +1

NeO 360: Neural Fields for Sparse View Synthesis of Outdoor Scenes

1 code implementation ICCV 2023 Muhammad Zubair Irshad, Sergey Zakharov, Katherine Liu, Vitor Guizilini, Thomas Kollar, Adrien Gaidon, Zsolt Kira, Rares Ambrus

NeO 360's representation allows us to learn from a large collection of unbounded 3D scenes while offering generalizability to new views and novel scenes from as few as a single image during inference.

Generalizable Novel View Synthesis Novel View Synthesis

Robust Self-Supervised Extrinsic Self-Calibration

no code implementations4 Aug 2023 Takayuki Kanai, Igor Vasiljevic, Vitor Guizilini, Adrien Gaidon, Rares Ambrus

Autonomous vehicles and robots need to operate over a wide variety of scenarios in order to complete tasks efficiently and safely.

Autonomous Vehicles Depth Prediction +2

NeRFuser: Large-Scale Scene Representation by NeRF Fusion

1 code implementation22 May 2023 Jiading Fang, Shengjie Lin, Igor Vasiljevic, Vitor Guizilini, Rares Ambrus, Adrien Gaidon, Gregory Shakhnarovich, Matthew R. Walter

A practical benefit of implicit visual representations like Neural Radiance Fields (NeRFs) is their memory efficiency: large scenes can be efficiently stored and shared as small neural nets instead of collections of images.

DeLiRa: Self-Supervised Depth, Light, and Radiance Fields

no code implementations ICCV 2023 Vitor Guizilini, Igor Vasiljevic, Jiading Fang, Rares Ambrus, Sergey Zakharov, Vincent Sitzmann, Adrien Gaidon

In this work, we propose to use the multi-view photometric objective from the self-supervised depth estimation literature as a geometric regularizer for volumetric rendering, significantly improving novel view synthesis without requiring additional information.

3D Reconstruction Depth Estimation +1

Viewpoint Equivariance for Multi-View 3D Object Detection

1 code implementation CVPR 2023 Dian Chen, Jie Li, Vitor Guizilini, Rares Ambrus, Adrien Gaidon

We design view-conditioned queries at the output level, which enables the generation of multiple virtual frames during training to learn viewpoint equivariance by enforcing multi-view consistency.

3D Object Detection Object +2

ROAD: Learning an Implicit Recursive Octree Auto-Decoder to Efficiently Encode 3D Shapes

no code implementations12 Dec 2022 Sergey Zakharov, Rares Ambrus, Katherine Liu, Adrien Gaidon

Compact and accurate representations of 3D shapes are central to many perception and robotics tasks.

ShaSTA: Modeling Shape and Spatio-Temporal Affinities for 3D Multi-Object Tracking

no code implementations8 Nov 2022 Tara Sadjadpour, Jie Li, Rares Ambrus, Jeannette Bohg

To address these issues in a unified framework, we propose to learn shape and spatio-temporal affinities between tracks and detections in consecutive frames.

3D Multi-Object Tracking Autonomous Vehicles +1

Photo-realistic Neural Domain Randomization

no code implementations23 Oct 2022 Sergey Zakharov, Rares Ambrus, Vitor Guizilini, Wadim Kehl, Adrien Gaidon

In this paper, we show that the recent progress in neural rendering enables a new unified approach we call Photo-realistic Neural Domain Randomization (PNDR).

Image Generation Monocular Depth Estimation +3

Depth Field Networks for Generalizable Multi-view Scene Representation

no code implementations28 Jul 2022 Vitor Guizilini, Igor Vasiljevic, Jiading Fang, Rares Ambrus, Greg Shakhnarovich, Matthew Walter, Adrien Gaidon

Modern 3D computer vision leverages learning to boost geometric reasoning, mapping image data to classical structures such as cost volumes or epipolar constraints to improve matching.

Data Augmentation Depth Estimation +2

Neural Groundplans: Persistent Neural Scene Representations from a Single Image

no code implementations22 Jul 2022 Prafull Sharma, Ayush Tewari, Yilun Du, Sergey Zakharov, Rares Ambrus, Adrien Gaidon, William T. Freeman, Fredo Durand, Joshua B. Tenenbaum, Vincent Sitzmann

We present a method to map 2D image observations of a scene to a persistent 3D scene representation, enabling novel view synthesis and disentangled representation of the movable and immovable components of the scene.

Disentanglement Instance Segmentation +4

SpOT: Spatiotemporal Modeling for 3D Object Tracking

no code implementations12 Jul 2022 Colton Stearns, Davis Rempe, Jie Li, Rares Ambrus, Sergey Zakharov, Vitor Guizilini, Yanchao Yang, Leonidas J Guibas

In this work, we develop a holistic representation of traffic scenes that leverages both spatial and temporal information of the actors in the scene.

3D Multi-Object Tracking 3D Object Tracking +1

Simple-BEV: What Really Matters for Multi-Sensor BEV Perception?

1 code implementation16 Jun 2022 Adam W. Harley, Zhaoyuan Fang, Jie Li, Rares Ambrus, Katerina Fragkiadaki

Building 3D perception systems for autonomous vehicles that do not rely on high-density LiDAR is a critical research problem because of the expense of LiDAR systems compared to cameras and other sensors.

Autonomous Vehicles Bird's-Eye View Semantic Segmentation +1

Multi-Frame Self-Supervised Depth with Transformers

no code implementations CVPR 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.

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?

2 code implementations 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 Moderate (using extra training data)

Monocular 3D Object Detection Monocular Depth Estimation +2

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.

Depth Prediction Monocular Depth Estimation +3

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

1 code implementation26 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 Management +5

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.

Depth Prediction Monocular Depth Estimation +3

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

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 +2

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 valid

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 +2

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 Object Discovery +1

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