1.4k citations · 3.9k across the 20 of their papers we have counts for
23 papers
MagicEyes: A Large Scale Eye Gaze Estimation Dataset for Mixed Reality
Zhengyang Wu, Srivignesh Rajendran, Tarrence van As +3
With the emergence of Virtual and Mixed Reality (XR) devices, eye tracking has received significant attention in the computer vision community. Eye gaze estimation is a crucial com…
Scan2Plan: Efficient Floorplan Generation from 3D Scans of Indoor Scenes
Ameya Phalak, Vijay Badrinarayanan, Andrew Rabinovich
We introduce Scan2Plan, a novel approach for accurate estimation of a floorplan from a 3D scan of the structural elements of indoor environments. The proposed method incorporates a…
Atlas: End-to-End 3D Scene Reconstruction from Posed Images
Zak Murez, Tarrence van As, James Bartolozzi +3
We present an end-to-end 3D reconstruction method for a scene by directly regressing a truncated signed distance function (TSDF) from a set of posed RGB images. Traditional approac…
DELTAS: Depth Estimation by Learning Triangulation And densification of Sparse points
Ayan Sinha, Zak Murez, James Bartolozzi +2
Multi-view stereo (MVS) is the golden mean between the accuracy of active depth sensing and the practicality of monocular depth estimation. Cost volume based approaches employing 3…
SuperGlue: Learning Feature Matching with Graph Neural Networks
Paul-Edouard Sarlin, Daniel DeTone, Tomasz Malisiewicz +1
This paper introduces SuperGlue, a neural network that matches two sets of local features by jointly finding correspondences and rejecting non-matchable points. Assignments are est…
Efficient 2.5D Hand Pose Estimation via Auxiliary Multi-Task Training for Embedded Devices
Prajwal Chidananda, Ayan Sinha, Adithya Rao +2
2D Key-point estimation is an important precursor to 3D pose estimation problems for human body and hands. In this work, we discuss the data, architecture, and training procedure n…
EyeNet: A Multi-Task Network for Off-Axis Eye Gaze Estimation and User Understanding
Zhengyang Wu, Srivignesh Rajendran, Tarrence van As +3
Eye gaze estimation and simultaneous semantic understanding of a user through eye images is a crucial component in Virtual and Mixed Reality; enabling energy efficient rendering, m…
DeepPerimeter: Indoor Boundary Estimation from Posed Monocular Sequences
Ameya Phalak, Zhao Chen, Darvin Yi +3
We present DeepPerimeter, a deep learning based pipeline for inferring a full indoor perimeter (i.e. exterior boundary map) from a sequence of posed RGB images. Our method relies o…
Self-Improving Visual Odometry
Daniel DeTone, Tomasz Malisiewicz, Andrew Rabinovich
We propose a self-supervised learning framework that uses unlabeled monocular video sequences to generate large-scale supervision for training a Visual Odometry (VO) frontend, a ne…
Gradient Adversarial Training of Neural Networks
Ayan Sinha, Zhao Chen, Vijay Badrinarayanan +1
We propose gradient adversarial training, an auxiliary deep learning framework applicable to different machine learning problems. In gradient adversarial training, we leverage a pr…
Estimating Depth from RGB and Sparse Sensing
Zhao Chen, Vijay Badrinarayanan, Gilad Drozdov +1
We present a deep model that can accurately produce dense depth maps given an RGB image with known depth at a very sparse set of pixels. The model works simultaneously for both ind…
SuperPoint: Self-Supervised Interest Point Detection and Description
Daniel DeTone, Tomasz Malisiewicz, Andrew Rabinovich
This paper presents a self-supervised framework for training interest point detectors and descriptors suitable for a large number of multiple-view geometry problems in computer vis…
GradNorm: Gradient Normalization for Adaptive Loss Balancing in Deep Multitask Networks
Zhao Chen, Vijay Badrinarayanan, Chen-Yu Lee +1
Deep multitask networks, in which one neural network produces multiple predictive outputs, can offer better speed and performance than their single-task counterparts but are challe…
Toward Geometric Deep SLAM
Daniel DeTone, Tomasz Malisiewicz, Andrew Rabinovich
We present a point tracking system powered by two deep convolutional neural networks. The first network, MagicPoint, operates on single images and extracts salient 2D points. The e…
RoomNet: End-to-End Room Layout Estimation
Chen-Yu Lee, Vijay Badrinarayanan, Tomasz Malisiewicz +1
This paper focuses on the task of room layout estimation from a monocular RGB image. Prior works break the problem into two sub-tasks: semantic segmentation of floor, walls, ceilin…
Deep Cuboid Detection: Beyond 2D Bounding Boxes
Debidatta Dwibedi, Tomasz Malisiewicz, Vijay Badrinarayanan +1
We present a Deep Cuboid Detector which takes a consumer-quality RGB image of a cluttered scene and localizes all 3D cuboids (box-like objects). Contrary to classical approaches wh…
Deep Image Homography Estimation
Daniel DeTone, Tomasz Malisiewicz, Andrew Rabinovich
We present a deep convolutional neural network for estimating the relative homography between a pair of images. Our feed-forward network has 10 layers, takes two stacked grayscale…
ParseNet: Looking Wider to See Better
Wei Liu, Andrew Rabinovich, Alexander C. Berg
We present a technique for adding global context to deep convolutional networks for semantic segmentation. The approach is simple, using the average feature for a layer to augment…
What's Cookin'? Interpreting Cooking Videos using Text, Speech and Vision
Jonathan Malmaud, Jonathan Huang, Vivek Rathod +3
We present a novel method for aligning a sequence of instructions to a video of someone carrying out a task. In particular, we focus on the cooking domain, where the instructions c…
Training Deep Neural Networks on Noisy Labels with Bootstrapping
Scott Reed, Honglak Lee, Dragomir Anguelov +3
Current state-of-the-art deep learning systems for visual object recognition and detection use purely supervised training with regularization such as dropout to avoid overfitting.…
Self-informed neural network structure learning
David Warde-Farley, Andrew Rabinovich, Dragomir Anguelov
We study the problem of large scale, multi-label visual recognition with a large number of possible classes. We propose a method for augmenting a trained neural network classifier…
Going Deeper with Convolutions
Christian Szegedy, Wei Liu, Yangqing Jia +6
We propose a deep convolutional neural network architecture codenamed "Inception", which was responsible for setting the new state of the art for classification and detection in th…
Using Web Co-occurrence Statistics for Improving Image Categorization
Samy Bengio, Jeff Dean, Dumitru Erhan +5
Object recognition and localization are important tasks in computer vision. The focus of this work is the incorporation of contextual information in order to improve object recogni…