224 citations · 312 across the 18 of their papers we have counts for
20 papers
FoundationStereo: Zero-Shot Stereo Matching
Bowen Wen, Matthew Trepte, Joseph Aribido +3
Tremendous progress has been made in deep stereo matching to excel on benchmark datasets through per-domain fine-tuning. However, achieving strong zero-shot generalization - a hall…
nvTorchCam: An Open-source Library for Camera-Agnostic Differentiable Geometric Vision
Daniel Lichy, Hang Su, Abhishek Badki +2
We introduce nvTorchCam, an open-source library under the Apache 2.0 license, designed to make deep learning algorithms camera model-independent. nvTorchCam abstracts critical came…
FoVA-Depth: Field-of-View Agnostic Depth Estimation for Cross-Dataset Generalization
Daniel Lichy, Hang Su, Abhishek Badki +2
Wide field-of-view (FoV) cameras efficiently capture large portions of the scene, which makes them attractive in multiple domains, such as automotive and robotics. For such applica…
Online Overexposed Pixels Hallucination in Videos with Adaptive Reference Frame Selection
Yazhou Xing, Amrita Mazumdar, Anjul Patney +5
Low dynamic range (LDR) cameras cannot deal with wide dynamic range inputs, frequently leading to local overexposure issues. We present a learning-based system to reduce these arti…
The Best Defense is a Good Offense: Adversarial Augmentation against Adversarial Attacks
Iuri Frosio, Jan Kautz
Many defenses against adversarial attacks (\eg robust classifiers, randomization, or image purification) use countermeasures put to work only after the attack has been crafted. We…
Binary TTC: A Temporal Geofence for Autonomous Navigation
Abhishek Badki, Orazio Gallo, Jan Kautz +1
Time-to-contact (TTC), the time for an object to collide with the observer's plane, is a powerful tool for path planning: it is potentially more informative than the depth, velocit…
Bi3D: Stereo Depth Estimation via Binary Classifications
Abhishek Badki, Alejandro Troccoli, Kihwan Kim +3
Stereo-based depth estimation is a cornerstone of computer vision, with state-of-the-art methods delivering accurate results in real time. For several applications such as autonomo…
How to Close Sim-Real Gap? Transfer with Segmentation!
Mengyuan Yan, Qingyun Sun, Iuri Frosio +2
One fundamental difficulty in robotic learning is the sim-real gap problem. In this work, we propose to use segmentation as the interface between perception and control, as a domai…
Meshlet Priors for 3D Mesh Reconstruction
Abhishek Badki, Orazio Gallo, Jan Kautz +1
Estimating a mesh from an unordered set of sparse, noisy 3D points is a challenging problem that requires carefully selected priors. Existing hand-crafted priors, such as smoothnes…
Extreme View Synthesis
Inchang Choi, Orazio Gallo, Alejandro Troccoli +2
We present Extreme View Synthesis, a solution for novel view extrapolation that works even when the number of input images is small--as few as two. In this context, occlusions and…
Tackling 3D ToF Artifacts Through Learning and the FLAT Dataset
Qi Guo, Iuri Frosio, Orazio Gallo +2
Scene motion, multiple reflections, and sensor noise introduce artifacts in the depth reconstruction performed by time-of-flight cameras. We propose a two-stage, deep-learning appr…
Reblur2Deblur: Deblurring Videos via Self-Supervised Learning
Huaijin Chen, Jinwei Gu, Orazio Gallo +3
Motion blur is a fundamental problem in computer vision as it impacts image quality and hinders inference. Traditional deblurring algorithms leverage the physics of the image forma…
Sim-to-Real Transfer of Accurate Grasping with Eye-In-Hand Observations and Continuous Control
Mengyuan Yan, Iuri Frosio, Stephen Tyree +1
In the context of deep learning for robotics, we show effective method of training a real robot to grasp a tiny sphere (1.37cm of diameter), with an original combination of system…
Separating Reflection and Transmission Images in the Wild
Patrick Wieschollek, Orazio Gallo, Jinwei Gu +1
The reflections caused by common semi-reflectors, such as glass windows, can impact the performance of computer vision algorithms. State-of-the-art methods can remove reflections o…
On Nearest Neighbors in Non Local Means Denoising
Iuri Frosio, Jan Kautz
To denoise a reference patch, the Non-Local-Means denoising filter processes a set of neighbor patches. Few Nearest Neighbors (NN) are used to limit the computational burden of the…
Deep Learning with Energy-efficient Binary Gradient Cameras
Suren Jayasuriya, Orazio Gallo, Jinwei Gu +1
Power consumption is a critical factor for the deployment of embedded computer vision systems. We explore the use of computational cameras that directly output binary gradient imag…
Reinforcement Learning through Asynchronous Advantage Actor-Critic on a GPU
Mohammad Babaeizadeh, Iuri Frosio, Stephen Tyree +2
We introduce a hybrid CPU/GPU version of the Asynchronous Advantage Actor-Critic (A3C) algorithm, currently the state-of-the-art method in reinforcement learning for various gaming…
Learning Adaptive Parameter Tuning for Image Processing
Jingming Dong, Iuri Frosio, Jan Kautz
The non-stationary nature of image characteristics calls for adaptive processing, based on the local image content. We propose a simple and flexible method to learn local tuning of…
Loss Functions for Neural Networks for Image Processing
Hang Zhao, Orazio Gallo, Iuri Frosio +1
Neural networks are becoming central in several areas of computer vision and image processing and different architectures have been proposed to solve specific problems. The impact…
Locally Non-rigid Registration for Mobile HDR Photography
Orazio Gallo, Alejandro Troccoli, Jun Hu +2
Image registration for stack-based HDR photography is challenging. If not properly accounted for, camera motion and scene changes result in artifacts in the composite image. Unfort…