45 citations · 85 across the 9 of their papers we have counts for
9 papers
3DMiner: Discovering Shapes from Large-Scale Unannotated Image Datasets
Ta-Ying Cheng, Matheus Gadelha, Soren Pirk +4
We present 3DMiner -- a pipeline for mining 3D shapes from challenging large-scale unannotated image datasets. Unlike other unsupervised 3D reconstruction methods, we assume that,…
Deep Learning for Visual Localization and Mapping: A Survey
Changhao Chen, Bing Wang, Chris Xiaoxuan Lu +2
Deep learning based localization and mapping approaches have recently emerged as a new research direction and receive significant attentions from both industry and academia. Instea…
Fusion of Radio and Camera Sensor Data for Accurate Indoor Positioning
Savvas Papaioannou, Hongkai Wen, Andrew Markham +1
Indoor positioning systems have received a lot of attention recently due to their importance for many location-based services, e.g. indoor navigation and smart buildings. Lightweig…
Tracking People in Highly Dynamic Industrial Environments
Savvas Papaioannou, Andrew Markham, Niki Trigoni
To date, the majority of positioning systems have been designed to operate within environments that have long-term stable macro-structure with potential small-scale dynamics. These…
When the Sun Goes Down: Repairing Photometric Losses for All-Day Depth Estimation
Madhu Vankadari, Stuart Golodetz, Sourav Garg +3
Self-supervised deep learning methods for joint depth and ego-motion estimation can yield accurate trajectories without needing ground-truth training data. However, as they typical…
Pose Adaptive Dual Mixup for Few-Shot Single-View 3D Reconstruction
Ta-Ying Cheng, Hsuan-Ru Yang, Niki Trigoni +2
We present a pose adaptive few-shot learning procedure and a two-stage data interpolation regularization, termed Pose Adaptive Dual Mixup (PADMix), for single-image 3D reconstructi…