Search Results for author:
Found 11 papers, 4 papers with code
Image-based Geolocalization by Ground-to-2.5D Map Matching
In this paper, we lift cross-view matching to a 2. 5D space, where heights of structures (e. g., trees and buildings) provide geometric information to guide the cross-view matching.
iDF-SLAM: End-to-End RGB-D SLAM with Neural Implicit Mapping and Deep Feature Tracking
The neural implicit mapper is trained on-the-fly, while though the neural tracker is pretrained on the ScanNet dataset, it is also finetuned along with the training of the neural implicit mapper.
Dual-Domain Image Synthesis using Segmentation-Guided GAN
We build on the successes of few-shot StyleGAN and single-shot semantic segmentation to minimise the amount of training required in utilising two domains.
FD-SLAM: 3-D Reconstruction Using Features and Dense Matching
It is well known that visual SLAM systems based on dense matching are locally accurate but are also susceptible to long-term drift and map corruption.
CGiS-Net: Aggregating Colour, Geometry and Implicit Semantic Features for Indoor Place Recognition
We describe a novel approach to indoor place recognition from RGB point clouds based on aggregating low-level colour and geometry features with high-level implicit semantic features.
Object-Augmented RGB-D SLAM for Wide-Disparity Relocalisation
During the map construction, we use a pre-trained neural network to detect objects and estimate 6D poses from RGB-D data.
You Are Here: Geolocation by Embedding Maps and Images
We present a novel approach to geolocalising panoramic images on a 2-D cartographic map based on learning a low dimensional embedded space, which allows a comparison between an image captured at a location and local neighbourhoods of the map.
Predicting Out-of-View Feature Points for Model-Based Camera Pose Estimation
In this work we present a novel framework that uses deep learning to predict object feature points that are out-of-view in the input image.
Automated Map Reading: Image Based Localisation in 2-D Maps Using Binary Semantic Descriptors
We describe a novel approach to image based localisation in urban environments using semantic matching between images and a 2-D map.
Visual place recognition using landmark distribution descriptors
Recent work by Suenderhauf et al. [1] demonstrated improved visual place recognition using proposal regions coupled with features from convolutional neural networks (CNN) to match landmarks between views.
HDRFusion: HDR SLAM using a low-cost auto-exposure RGB-D sensor
We describe a new method for comparing frame appearance in a frame-to-model 3-D mapping and tracking system using an low dynamic range (LDR) RGB-D camera which is robust to brightness changes caused by auto exposure.
