Search Results for author:
Found 24 papers, 14 papers with code
Atlas: End-to-End 3D Scene Reconstruction from Posed Images
Traditional approaches to 3D reconstruction rely on an intermediate representation of depth maps prior to estimating a full 3D model of a scene.
Ranked #1 on
3D Reconstruction
on ScanNet
DELTAS: Depth Estimation by Learning Triangulation And densification of Sparse points
Cost volume based approaches employing 3D convolutional neural networks (CNNs) have considerably improved the accuracy of MVS systems.
Ranked #1 on
Depth Estimation
on ScanNetV2
MagicEyes: A Large Scale Eye Gaze Estimation Dataset for Mixed Reality
With the emergence of Virtual and Mixed Reality (XR) devices, eye tracking has received significant attention in the computer vision community.
Scan2Plan: Efficient Floorplan Generation from 3D Scans of Indoor Scenes
We introduce Scan2Plan, a novel approach for accurate estimation of a floorplan from a 3D scan of the structural elements of indoor environments.
SuperGlue: Learning Feature Matching with Graph Neural Networks
This paper introduces SuperGlue, a neural network that matches two sets of local features by jointly finding correspondences and rejecting non-matchable points.
Ranked #2 on
Visual Place Recognition
on Berlin Kudamm
Efficient 2.5D Hand Pose Estimation via Auxiliary Multi-Task Training for Embedded Devices
2D Key-point estimation is an important precursor to 3D pose estimation problems for human body and hands.
EyeNet: A Multi-Task Network for Off-Axis Eye Gaze Estimation and User Understanding
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, multi-focal displays and effective interaction with 3D content.
DeepPerimeter: Indoor Boundary Estimation from Posed Monocular Sequences
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.
Self-Improving Visual Odometry
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 network which computes pointwise data associations across images.
Gradient Adversarial Training of Neural Networks
We demonstrate gradient adversarial training for three different scenarios: (1) as a defense to adversarial examples we classify gradient tensors and tune them to be agnostic to the class of their corresponding example, (2) for knowledge distillation, we do binary classification of gradient tensors derived from the student or teacher network and tune the student gradient tensor to mimic the teacher's gradient tensor; and (3) for multi-task learning we classify the gradient tensors derived from different task loss functions and tune them to be statistically indistinguishable.
Estimating Depth from RGB and Sparse Sensing
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.
SuperPoint: Self-Supervised Interest Point Detection and Description
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 vision.
GradNorm: Gradient Normalization for Adaptive Loss Balancing in Deep Multitask Networks
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 challenging to train properly.
Toward Geometric Deep SLAM
The first network, MagicPoint, operates on single images and extracts salient 2D points.
RoomNet: End-to-End Room Layout Estimation
This paper focuses on the task of room layout estimation from a monocular RGB image.
Deep Cuboid Detection: Beyond 2D Bounding Boxes
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).
Deep Image Homography Estimation
We present a deep convolutional neural network for estimating the relative homography between a pair of images.
Ranked #3 on
Homography Estimation
on PDS-COCO
ParseNet: Looking Wider to See Better
When we add our proposed global feature, and a technique for learning normalization parameters, accuracy increases consistently even over our improved versions of the baselines.
Ranked #39 on
Semantic Segmentation
on PASCAL VOC 2012 test
What's Cookin'? Interpreting Cooking Videos using Text, Speech and Vision
We present a novel method for aligning a sequence of instructions to a video of someone carrying out a task.
Self-informed neural network structure learning
We study the problem of large scale, multi-label visual recognition with a large number of possible classes.
Training Deep Neural Networks on Noisy Labels with Bootstrapping
On MNIST handwritten digits, we show that our model is robust to label corruption.
Going Deeper with Convolutions
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 the ImageNet Large-Scale Visual Recognition Challenge 2014 (ILSVRC 2014).
Using Web Co-occurrence Statistics for Improving Image Categorization
Albeit the simplicity of the resulting optimization problem, it is effective in improving both recognition and localization accuracy.
