Search Results for author:
Found 17 papers, 5 papers with code
DF-VO: What Should Be Learnt for Visual Odometry?
More surprisingly, they show that the well-trained networks enable scale-consistent predictions over long videos, while the accuracy is still inferior to traditional methods because of ignoring geometric information.
Unsupervised Learning of Monocular Depth Estimation and Visual Odometry with Deep Feature Reconstruction
Despite learning based methods showing promising results in single view depth estimation and visual odometry, most existing approaches treat the tasks in a supervised manner.
Unsupervised CNN for Single View Depth Estimation: Geometry to the Rescue
In this work we propose a unsupervised framework to learn a deep convolutional neural network for single view depth predic- tion, without requiring a pre-training stage or annotated ground truth depths.
Adaptive Low-Rank Kernel Subspace Clustering
In this paper, we present a kernel subspace clustering method that can handle non-linear models.
Addressing Challenging Place Recognition Tasks using Generative Adversarial Networks
In the process, meaningful feature spaces are learned for each domain, the distances in which can be used for the task of place recognition.
Robotics
Just-in-Time Reconstruction: Inpainting Sparse Maps using Single View Depth Predictors as Priors
Crucially, we obtain the confidence weights that parameterize the CRF model in a data-dependent manner via Convolutional Neural Networks (CNNs) which are trained to model the conditional depth error distributions given each source of input depth map and the associated RGB image.
Learning Deeply Supervised Good Features to Match for Dense Monocular Reconstruction
Visual SLAM (Simultaneous Localization and Mapping) methods typically rely on handcrafted visual features or raw RGB values for establishing correspondences between images.
A Bootstrap Machine Learning Approach to Identify Rare Disease Patients from Electronic Health Records
Rare diseases are very difficult to identify among large number of other possible diagnoses.
Non-linear Dimensionality Regularizer for Solving Inverse Problems
Additionally, we evaluate our method on the challenging problem of Non-Rigid Structure from Motion and our approach delivers promising results on CMU mocap dataset despite the presence of significant occlusions and noise.
Single-view Object Shape Reconstruction Using Deep Shape Prior and Silhouette
3D shape reconstruction from a single image is a highly ill-posed problem.
Dense Variational Reconstruction of Non-rigid Surfaces from Monocular Video
This paper offers the first variational approach to the problem of dense 3D reconstruction of non-rigid surfaces from a monocular video sequence.
Self-supervised Learning for Single View Depth and Surface Normal Estimation
In this work we present a self-supervised learning framework to simultaneously train two Convolutional Neural Networks (CNNs) to predict depth and surface normals from a single image.
Ranked #61 on
Monocular Depth Estimation
on KITTI Eigen split
Non-Parametric Priors For Generative Adversarial Networks
The advent of generative adversarial networks (GAN) has enabled new capabilities in synthesis, interpolation, and data augmentation heretofore considered very challenging.
Improved Visual Localization via Graph Smoothing
One solution to this problem is to learn a deep neural network to infer the pose of a query image after learning on a dataset of images with known poses.
Feasibility Study on Intra-Grid Location Estimation Using Power ENF Signals
In this study, we demonstrate that it is possible to pinpoint the location-of-recording to a certain geographical resolution using power signal recordings containing strong ENF traces.
Retrieval Augmented Classification for Long-Tail Visual Recognition
We introduce Retrieval Augmented Classification (RAC), a generic approach to augmenting standard image classification pipelines with an explicit retrieval module.
Ranked #4 on
Long-tail Learning
on iNaturalist 2018
Semi-Supervised Semantic Segmentation under Label Noise via Diverse Learning Groups
Each learning group consists of a teacher network, a student network and a novel filter module.
