1 code implementation • 11 Aug 2023 • Mengjie Zhou, Liu Liu, Yiran Zhong, Andrew Calway
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.
no code implementations • 16 Sep 2022 • Yuhang Ming, Weicai Ye, Andrew Calway
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.
1 code implementation • 19 Apr 2022 • Dena Bazazian, Andrew Calway, Dima Damen
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.
no code implementations • 25 Mar 2022 • Xingrui Yang, Yuhang Ming, Zhaopeng Cui, Andrew Calway
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.
1 code implementation • 4 Feb 2022 • Yuhang Ming, Xingrui Yang, Guofeng Zhang, Andrew Calway
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.
1 code implementation • 5 Aug 2021 • Yuhang Ming, Xingrui Yang, Andrew Calway
During the map construction, we use a pre-trained neural network to detect objects and estimate 6D poses from RGB-D data.
no code implementations • ECCV 2020 • Noe Samano, Mengjie Zhou, Andrew Calway
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.
no code implementations • 5 Mar 2018 • Oliver Moolan-Feroze, Andrew Calway
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.
no code implementations • 2 Mar 2018 • Pilailuck Panphattarasap, Andrew Calway
We describe a novel approach to image based localisation in urban environments using semantic matching between images and a 2-D map.
no code implementations • 15 Aug 2016 • Pilailuck Panphattarasap, Andrew Calway
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.
no code implementations • 4 Apr 2016 • Shuda Li, Ankur Handa, Yang Zhang, Andrew Calway
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.