b'Or Litany'

research

∙ 09/11/2023

Towards Viewpoint Robustness in Bird's Eye View Segmentation

Autonomous vehicles (AV) require that neural networks used for perceptio...

0 Tzofi Klinghoffer, et al. ∙

research

∙ 08/26/2023

Semi-Supervised Semantic Segmentation via Marginal Contextual Information

We present a novel confidence refinement scheme that enhances pseudo-lab...

0 Moshe Kimhi, et al. ∙

research

∙ 05/22/2023

Fast Monocular Scene Reconstruction with Global-Sparse Local-Dense Grids

Indoor scene reconstruction from monocular images has long been sought a...

2 Wei Dong, et al. ∙

research

∙ 05/02/2023

Neural LiDAR Fields for Novel View Synthesis

We present Neural Fields for LiDAR (NFL), a method to optimise a neural ...

5 Shengyu Huang, et al. ∙

research

∙ 03/25/2023

UnScene3D: Unsupervised 3D Instance Segmentation for Indoor Scenes

3D instance segmentation is fundamental to geometric understanding of th...

1 David Rozenberszki, et al. ∙

research

∙ 10/12/2022

LION: Latent Point Diffusion Models for 3D Shape Generation

Denoising diffusion models (DDMs) have shown promising results in 3D poi...

10 Xiaohui Zeng, et al. ∙

research

∙ 10/06/2022

Mask3D for 3D Semantic Instance Segmentation

Modern 3D semantic instance segmentation approaches predominantly rely o...

10 Jonas Schult, et al. ∙

research

∙ 09/22/2022

GET3D: A Generative Model of High Quality 3D Textured Shapes Learned from Images

As several industries are moving towards modeling massive 3D virtual wor...

27 Jun Gao, et al. ∙

research

∙ 08/18/2022

MvDeCor: Multi-view Dense Correspondence Learning for Fine-grained 3D Segmentation

We propose to utilize self-supervised techniques in the 2D domain for fi...

18 Gopal Sharma, et al. ∙

research

∙ 04/16/2022

Language-Grounded Indoor 3D Semantic Segmentation in the Wild

Recent advances in 3D semantic segmentation with deep neural networks ha...

20 David Rozenberszki, et al. ∙

research

∙ 02/16/2022

Learning Smooth Neural Functions via Lipschitz Regularization

Neural implicit fields have recently emerged as a useful representation ...

3 Hsueh-Ti Derek Liu, et al. ∙

research

∙ 02/08/2022

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

Modern computer vision applications rely on learning-based perception mo...

7 Cinjon Resnick, et al. ∙

research

∙ 01/20/2022

Federated Learning with Heterogeneous Architectures using Graph HyperNetworks

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

14 Or Litany, et al. ∙

research

∙ 12/09/2021

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

Evaluating and improving planning for autonomous vehicles requires scala...

8 Davis Rempe, et al. ∙

research

∙ 11/26/2021

Neural Fields as Learnable Kernels for 3D Reconstruction

We present Neural Kernel Fields: a novel method for reconstructing impli...

27 Francis Williams, et al. ∙

research

∙ 11/22/2021

Neural Fields in Visual Computing and Beyond

Recent advances in machine learning have created increasing interest in ...

4 Yiheng Xie, et al. ∙

research

∙ 10/30/2021

DIB-R++: Learning to Predict Lighting and Material with a Hybrid Differentiable Renderer

We consider the challenging problem of predicting intrinsic object prope...

4 Wenzheng Chen, et al. ∙

research

∙ 10/05/2021

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

We present Mix3D, a data augmentation technique for segmenting large-sca...

6 Alexey Nekrasov, et al. ∙

research

∙ 05/17/2021

StrobeNet: Category-Level Multiview Reconstruction of Articulated Objects

We present StrobeNet, a method for category-level 3D reconstruction of a...

8 Ge Zhang, et al. ∙

research

∙ 04/25/2021

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

Invariance and equivariance to the rotation group have been widely discu...

11 Congyue Deng, et al. ∙

research

∙ 03/31/2021

Spectral Unions of Partial Deformable 3D Shapes

Spectral geometric methods have brought revolutionary changes to the fie...

9 Luca Moschella, et al. ∙

research

∙ 03/25/2021

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

The success of learning with noisy labels (LNL) methods relies heavily o...

4 Evgenii Zheltonozhskii, et al. ∙

research

∙ 02/17/2021

Weakly Supervised Learning of Rigid 3D Scene Flow

We propose a data-driven scene flow estimation algorithm exploiting the ...

4 Zan Gojcic, et al. ∙

research

∙ 02/01/2021

Self-Supervised Equivariant Scene Synthesis from Video

We propose a self-supervised framework to learn scene representations fr...

8 Cinjon Resnick, et al. ∙

research

∙ 12/18/2020

Human 3D keypoints via spatial uncertainty modeling

We introduce a technique for 3D human keypoint estimation that directly ...

5 Francis Williams, et al. ∙

research

∙ 12/08/2020

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

3D object detection is an important yet demanding task that heavily reli...

15 He Wang, et al. ∙

research

∙ 11/26/2020

Non-Rigid Puzzles

Shape correspondence is a fundamental problem in computer graphics and v...

5 Or Litany, et al. ∙

research

∙ 11/11/2020

Learned Equivariant Rendering without Transformation Supervision

We propose a self-supervised framework to learn scene representations fr...

4 Cinjon Resnick, et al. ∙

research

∙ 08/18/2020

ReLMoGen: Leveraging Motion Generation in Reinforcement Learning for Mobile Manipulation

Many Reinforcement Learning (RL) approaches use joint control signals (p...

2 Fei Xia, et al. ∙

research

∙ 07/21/2020

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

Arguably one of the top success stories of deep learning is transfer lea...

11 Saining Xie, et al. ∙

research

∙ 07/20/2020

Object-Centric Multi-View Aggregation

We present an approach for aggregating a sparse set of views of an objec...

18 Shubham Tulsiani, et al. ∙

research

∙ 02/26/2020

Representation Learning Through Latent Canonicalizations

We seek to learn a representation on a large annotated data source that ...

21 Or Litany, et al. ∙

research

∙ 02/20/2020

On Learning Sets of Symmetric Elements

Learning from unordered sets is a fundamental learning setup, which is a...

21 Haggai Maron, et al. ∙

research

∙ 02/06/2020

Continuous Geodesic Convolutions for Learning on 3D Shapes

The majority of descriptor-based methods for geometric processing of non...

15 Zhangsihao Yang, et al. ∙

research

∙ 01/29/2020

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

3D object detection has seen quick progress thanks to advances in deep l...

15 Charles R. Qi, et al. ∙

research

∙ 01/27/2020

The Whole Is Greater Than the Sum of Its Nonrigid Parts

According to Aristotle, a philosopher in Ancient Greece, "the whole is g...

11 Oshri Halimi, et al. ∙

research

∙ 04/21/2019

Deep Hough Voting for 3D Object Detection in Point Clouds

Current 3D object detection methods are heavily influenced by 2D detecto...

18 Charles R. Qi, et al. ∙

research

∙ 12/06/2018

Self-supervised Learning of Dense Shape Correspondence

We introduce the first completely unsupervised correspondence learning a...

6 Oshri Halimi, et al. ∙

research

∙ 08/20/2018

Class-Aware Fully-Convolutional Gaussian and Poisson Denoising

We propose a fully-convolutional neural-network architecture for image d...

0 Tal Remez, et al. ∙

research

∙ 06/03/2018

Dual-Primal Graph Convolutional Networks

In recent years, there has been a surge of interest in developing deep l...

0 Federico Monti, et al. ∙

research

∙ 05/24/2018

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

We present SOSELETO (SOurce SELEction for Target Optimization), a new me...

0 Or Litany, et al. ∙

research

∙ 12/01/2017

Deformable Shape Completion with Graph Convolutional Autoencoders

The availability of affordable and portable depth sensors has made scann...

0 Or Litany, et al. ∙

research

∙ 07/25/2017

Efficient Deformable Shape Correspondence via Kernel Matching

We present a method to match three dimensional shapes under non-isometri...

0 Zorah Lähner, et al. ∙

research

∙ 01/06/2017

Deep Class Aware Denoising

The increasing demand for high image quality in mobile devices brings fo...

0 Tal Remez, et al. ∙

research

∙ 01/06/2017

Deep Convolutional Denoising of Low-Light Images

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

0 Tal Remez, et al. ∙

research

∙ 12/15/2016

Cloud Dictionary: Sparse Coding and Modeling for Point Clouds

With the development of range sensors such as LIDAR and time-of-flight c...

0 Or Litany, et al. ∙

research

∙ 08/03/2016

FPGA system for real-time computational extended depth of field imaging using phase aperture coding

We present a proof-of-concept end-to-end system for computational extend...

0 Tal Remez, et al. ∙

research

∙ 12/06/2015

Image reconstruction from dense binary pixels

Recently, the dense binary pixel Gigavision camera had been introduced, ...

0 Or Litany, et al. ∙

research

∙ 12/04/2015

ASIST: Automatic Semantically Invariant Scene Transformation

We present ASIST, a technique for transforming point clouds by replacing...

0 Or Litany, et al. ∙

research

∙ 10/15/2015

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

The pursuit of smaller pixel sizes at ever increasing resolution in digi...

0 Tal Remez, et al. ∙

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