136 citations · 327 across the 11 of their papers we have counts for
13 papers · 1 filter
Towards Semantic Segmentation of Urban-Scale 3D Point Clouds: A Dataset, Benchmarks and Challenges
Qingyong Hu, Bo Yang, Sheikh Khalid +3
An essential prerequisite for unleashing the potential of supervised deep learning algorithms in the area of 3D scene understanding is the availability of large-scale and richly an…
A Survey on Deep Learning for Localization and Mapping: Towards the Age of Spatial Machine Intelligence
Changhao Chen, Bing Wang, Chris Xiaoxuan Lu +2
Deep learning based localization and mapping has recently attracted significant attention. Instead of creating hand-designed algorithms through exploitation of physical models or g…
RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Clouds
Qingyong Hu, Bo Yang, Linhai Xie +5
We study the problem of efficient semantic segmentation for large-scale 3D point clouds. By relying on expensive sampling techniques or computationally heavy pre/post-processing st…
SelfVIO: Self-Supervised Deep Monocular Visual-Inertial Odometry and Depth Estimation
Yasin Almalioglu, Mehmet Turan, Alp Eren Sari +4
In the last decade, numerous supervised deep learning approaches requiring large amounts of labeled data have been proposed for visual-inertial odometry (VIO) and depth map estimat…
AtLoc: Attention Guided Camera Localization
Bing Wang, Changhao Chen, Chris Xiaoxuan Lu +3
Deep learning has achieved impressive results in camera localization, but current single-image techniques typically suffer from a lack of robustness, leading to large outliers. To…
DeepPCO: End-to-End Point Cloud Odometry through Deep Parallel Neural Network
Wei Wang, Muhamad Risqi U. Saputra, Peijun Zhao +5
Odometry is of key importance for localization in the absence of a map. There is considerable work in the area of visual odometry (VO), and recent advances in deep learning have br…