Search Results for author: Or Litany

Found 44 papers, 20 papers with code

Towards Precise Completion of Deformable Shapes

1 code implementation ECCV 2020 Oshri Halimi, Ido Imanuel, Or Litany, Giovanni Trappolini, Emanuele Rodolà, Leonidas Guibas, Ron Kimmel

Here, we claim that observing part of an object which was previously acquired as a whole, one could deal with both partial matching and shape completion in a holistic manner.

Language-Grounded Indoor 3D Semantic Segmentation in the Wild

no code implementations16 Apr 2022 David Rozenberszki, Or Litany, Angela Dai

Recent advances in 3D semantic segmentation with deep neural networks have shown remarkable success, with rapid performance increase on available datasets.

3D Semantic Segmentation

Learning Smooth Neural Functions via Lipschitz Regularization

no code implementations16 Feb 2022 Hsueh-Ti Derek Liu, Francis Williams, Alec Jacobson, Sanja Fidler, Or Litany

The latent descriptor of a neural field acts as a deformation handle for the 3D shape it represents.

Causal Scene BERT: Improving object detection by searching for challenging groups of data

no code implementations8 Feb 2022 Cinjon Resnick, Or Litany, Amlan Kar, Karsten Kreis, James Lucas, Kyunghyun Cho, Sanja Fidler

Our main contribution is a pseudo-automatic method to discover such groups in foresight by performing causal interventions on simulated scenes.

Autonomous Vehicles Object Detection

Federated Learning with Heterogeneous Architectures using Graph HyperNetworks

no code implementations20 Jan 2022 Or Litany, Haggai Maron, David Acuna, Jan Kautz, Gal Chechik, Sanja Fidler

Standard Federated Learning (FL) techniques are limited to clients with identical network architectures.

Federated Learning

Generating Useful Accident-Prone Driving Scenarios via a Learned Traffic Prior

no code implementations9 Dec 2021 Davis Rempe, Jonah Philion, Leonidas J. Guibas, Sanja Fidler, Or Litany

Scenario generation is formulated as an optimization in the latent space of this traffic model, perturbing an initial real-world scene to produce trajectories that collide with a given planner.

Autonomous Vehicles

Neural Fields as Learnable Kernels for 3D Reconstruction

no code implementations26 Nov 2021 Francis Williams, Zan Gojcic, Sameh Khamis, Denis Zorin, Joan Bruna, Sanja Fidler, Or Litany

We present Neural Kernel Fields: a novel method for reconstructing implicit 3D shapes based on a learned kernel ridge regression.

3D Reconstruction

Neural Fields in Visual Computing and Beyond

no code implementations22 Nov 2021 Yiheng Xie, Towaki Takikawa, Shunsuke Saito, Or Litany, Shiqin Yan, Numair Khan, Federico Tombari, James Tompkin, Vincent Sitzmann, Srinath Sridhar

Recent advances in machine learning have created increasing interest in solving visual computing problems using a class of coordinate-based neural networks that parametrize physical properties of scenes or objects across space and time.

3D Reconstruction Image Animation +1

Mix3D: Out-of-Context Data Augmentation for 3D Scenes

1 code implementation5 Oct 2021 Alexey Nekrasov, Jonas Schult, Or Litany, Bastian Leibe, Francis Engelmann

Since scene context helps reasoning about object semantics, current works focus on models with large capacity and receptive fields that can fully capture the global context of an input 3D scene.

3D Semantic Segmentation

Causal Scene BERT: Improving object detection by searching for challenging groups

no code implementations29 Sep 2021 Cinjon Resnick, Or Litany, Amlan Kar, Karsten Kreis, James Lucas, Kyunghyun Cho, Sanja Fidler

We verify that the prioritized groups found via intervention are challenging for the object detector and show that retraining with data collected from these groups helps inordinately compared to adding more IID data.

Autonomous Vehicles Object Detection

StrobeNet: Category-Level Multiview Reconstruction of Articulated Objects

no code implementations17 May 2021 Ge Zhang, Or Litany, Srinath Sridhar, Leonidas Guibas

We present StrobeNet, a method for category-level 3D reconstruction of articulating objects from one or more unposed RGB images.

3D Reconstruction

Vector Neurons: A General Framework for SO(3)-Equivariant Networks

3 code implementations ICCV 2021 Congyue Deng, Or Litany, Yueqi Duan, Adrien Poulenard, Andrea Tagliasacchi, Leonidas Guibas

Invariance and equivariance to the rotation group have been widely discussed in the 3D deep learning community for pointclouds.

Learning Spectral Unions of Partial Deformable 3D Shapes

1 code implementation31 Mar 2021 Luca Moschella, Simone Melzi, Luca Cosmo, Filippo Maggioli, Or Litany, Maks Ovsjanikov, Leonidas Guibas, Emanuele Rodolà

Spectral geometric methods have brought revolutionary changes to the field of geometry processing.

Contrast to Divide: Self-Supervised Pre-Training for Learning with Noisy Labels

1 code implementation25 Mar 2021 Evgenii Zheltonozhskii, Chaim Baskin, Avi Mendelson, Alex M. Bronstein, Or Litany

In this paper, we identify a "warm-up obstacle": the inability of standard warm-up stages to train high quality feature extractors and avert memorization of noisy labels.

Learning with noisy labels

Weakly Supervised Learning of Rigid 3D Scene Flow

1 code implementation CVPR 2021 Zan Gojcic, Or Litany, Andreas Wieser, Leonidas J. Guibas, Tolga Birdal

We propose a data-driven scene flow estimation algorithm exploiting the observation that many 3D scenes can be explained by a collection of agents moving as rigid bodies.

Autonomous Driving Scene Flow Estimation +1

Self-Supervised Equivariant Scene Synthesis from Video

no code implementations1 Feb 2021 Cinjon Resnick, Or Litany, Cosmas Heiß, Hugo Larochelle, Joan Bruna, Kyunghyun Cho

We propose a self-supervised framework to learn scene representations from video that are automatically delineated into background, characters, and their animations.

Human 3D keypoints via spatial uncertainty modeling

no code implementations18 Dec 2020 Francis Williams, Or Litany, Avneesh Sud, Kevin Swersky, Andrea Tagliasacchi

We introduce a technique for 3D human keypoint estimation that directly models the notion of spatial uncertainty of a keypoint.

3DIoUMatch: Leveraging IoU Prediction for Semi-Supervised 3D Object Detection

2 code implementations CVPR 2021 He Wang, Yezhen Cong, Or Litany, Yue Gao, Leonidas J. Guibas

On KITTI, we are the first to demonstrate semi-supervised 3D object detection and our method surpasses a fully supervised baseline from 1. 8% to 7. 6% under different label ratios and categories.

3D Object Detection Autonomous Driving

Non-Rigid Puzzles

no code implementations26 Nov 2020 Or Litany, Emanuele Rodolà, Alex Bronstein, Michael Bronstein, Daniel Cremers

We assume to be given a reference shape and its multiple parts undergoing a non-rigid deformation.

Learned Equivariant Rendering without Transformation Supervision

no code implementations11 Nov 2020 Cinjon Resnick, Or Litany, Hugo Larochelle, Joan Bruna, Kyunghyun Cho

We propose a self-supervised framework to learn scene representations from video that are automatically delineated into objects and background.

ReLMoGen: Leveraging Motion Generation in Reinforcement Learning for Mobile Manipulation

no code implementations18 Aug 2020 Fei Xia, Chengshu Li, Roberto Martín-Martín, Or Litany, Alexander Toshev, Silvio Savarese

To validate our method, we apply ReLMoGen to two types of tasks: 1) Interactive Navigation tasks, navigation problems where interactions with the environment are required to reach the destination, and 2) Mobile Manipulation tasks, manipulation tasks that require moving the robot base.

Continuous Control Hierarchical Reinforcement Learning +1

PointContrast: Unsupervised Pre-training for 3D Point Cloud Understanding

1 code implementation ECCV 2020 Saining Xie, Jiatao Gu, Demi Guo, Charles R. Qi, Leonidas J. Guibas, Or Litany

To this end, we select a suite of diverse datasets and tasks to measure the effect of unsupervised pre-training on a large source set of 3D scenes.

Point Cloud Pre-training Representation Learning +3

Object-Centric Multi-View Aggregation

no code implementations20 Jul 2020 Shubham Tulsiani, Or Litany, Charles R. Qi, He Wang, Leonidas J. Guibas

We present an approach for aggregating a sparse set of views of an object in order to compute a semi-implicit 3D representation in the form of a volumetric feature grid.

Novel View Synthesis Pose Estimation

Representation Learning Through Latent Canonicalizations

no code implementations26 Feb 2020 Or Litany, Ari Morcos, Srinath Sridhar, Leonidas Guibas, Judy Hoffman

We seek to learn a representation on a large annotated data source that generalizes to a target domain using limited new supervision.

Disentanglement

On Learning Sets of Symmetric Elements

2 code implementations ICML 2020 Haggai Maron, Or Litany, Gal Chechik, Ethan Fetaya

We first characterize the space of linear layers that are equivariant both to element reordering and to the inherent symmetries of elements, like translation in the case of images.

3D Shape Recognition Deblurring +1

Continuous Geodesic Convolutions for Learning on 3D Shapes

no code implementations6 Feb 2020 Zhangsihao Yang, Or Litany, Tolga Birdal, Srinath Sridhar, Leonidas Guibas

In this work, we wish to challenge this practice and use a neural network to learn descriptors directly from the raw mesh.

Frame

ImVoteNet: Boosting 3D Object Detection in Point Clouds with Image Votes

1 code implementation CVPR 2020 Charles R. Qi, Xinlei Chen, Or Litany, Leonidas J. Guibas

Compared to prior work on multi-modal detection, we explicitly extract both geometric and semantic features from the 2D images.

 Ranked #1 on 3D Object Detection on SUN-RGBD (using extra training data)

3D Object Detection

The Whole Is Greater Than the Sum of Its Nonrigid Parts

1 code implementation27 Jan 2020 Oshri Halimi, Ido Imanuel, Or Litany, Giovanni Trappolini, Emanuele Rodolà, Leonidas Guibas, Ron Kimmel

Here, we claim that observing part of an object which was previously acquired as a whole, one could deal with both partial matching and shape completion in a holistic manner.

Self-supervised Learning of Dense Shape Correspondence

1 code implementation6 Dec 2018 Oshri Halimi, Or Litany, Emanuele Rodolà, Alex Bronstein, Ron Kimmel

The resulting learning model is class-agnostic, and is able to leverage any type of deformable geometric data for the training phase.

Self-Supervised Learning

Class-Aware Fully-Convolutional Gaussian and Poisson Denoising

2 code implementations20 Aug 2018 Tal Remez, Or Litany, Raja Giryes, Alex M. Bronstein

We propose a fully-convolutional neural-network architecture for image denoising which is simple yet powerful.

Image Denoising

Dual-Primal Graph Convolutional Networks

no code implementations3 Jun 2018 Federico Monti, Oleksandr Shchur, Aleksandar Bojchevski, Or Litany, Stephan Günnemann, Michael M. Bronstein

In recent years, there has been a surge of interest in developing deep learning methods for non-Euclidean structured data such as graphs.

Graph Attention Recommendation Systems

SOSELETO: A Unified Approach to Transfer Learning and Training with Noisy Labels

1 code implementation ICLR 2019 Or Litany, Daniel Freedman

We present SOSELETO (SOurce SELEction for Target Optimization), a new method for exploiting a source dataset to solve a classification problem on a target dataset.

Bilevel Optimization Classification +2

Efficient Deformable Shape Correspondence via Kernel Matching

1 code implementation25 Jul 2017 Zorah Lähner, Matthias Vestner, Amit Boyarski, Or Litany, Ron Slossberg, Tal Remez, Emanuele Rodolà, Alex Bronstein, Michael Bronstein, Ron Kimmel, Daniel Cremers

We present a method to match three dimensional shapes under non-isometric deformations, topology changes and partiality.

Deep Class Aware Denoising

1 code implementation6 Jan 2017 Tal Remez, Or Litany, Raja Giryes, Alex M. Bronstein

We further show that a significant boost in performance of up to $0. 4$ dB PSNR can be achieved by making our network class-aware, namely, by fine-tuning it for images belonging to a specific semantic class.

Image Denoising Image Enhancement

Deep Convolutional Denoising of Low-Light Images

2 code implementations6 Jan 2017 Tal Remez, Or Litany, Raja Giryes, Alex M. Bronstein

Poisson distribution is used for modeling noise in photon-limited imaging.

Denoising

Cloud Dictionary: Sparse Coding and Modeling for Point Clouds

3 code implementations15 Dec 2016 Or Litany, Tal Remez, Alex Bronstein

With the development of range sensors such as LIDAR and time-of-flight cameras, 3D point cloud scans have become ubiquitous in computer vision applications, the most prominent ones being gesture recognition and autonomous driving.

Autonomous Driving Denoising +1

Image reconstruction from dense binary pixels

no code implementations6 Dec 2015 Or Litany, Tal Remez, Alex Bronstein

Recently, the dense binary pixel Gigavision camera had been introduced, emulating a digital version of the photographic film.

Image Reconstruction

ASIST: Automatic Semantically Invariant Scene Transformation

no code implementations4 Dec 2015 Or Litany, Tal Remez, Daniel Freedman, Lior Shapira, Alex Bronstein, Ran Gal

We present ASIST, a technique for transforming point clouds by replacing objects with their semantically equivalent counterparts.

A Picture is Worth a Billion Bits: Real-Time Image Reconstruction from Dense Binary Pixels

no code implementations15 Oct 2015 Tal Remez, Or Litany, Alex Bronstein

In this work, we study a variant of a sensor with binary threshold pixels and propose a reconstruction algorithm combining an ML data fitting term with a sparse synthesis prior.

Image Reconstruction Quantization

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