79 citations · 285 across the 13 of their papers we have counts for
6 papers · 1 filter
Push-the-Boundary: Boundary-aware Feature Propagation for Semantic Segmentation of 3D Point Clouds
Shenglan Du, Nail Ibrahimli, Jantien Stoter +2
Feedforward fully convolutional neural networks currently dominate in semantic segmentation of 3D point clouds. Despite their great success, they suffer from the loss of local info…
3DLG-Detector: 3D Object Detection via Simultaneous Local-Global Feature Learning
Baian Chen, Liangliang Nan, Haoran Xie +3
Capturing both local and global features of irregular point clouds is essential to 3D object detection (3OD). However, mainstream 3D detectors, e.g., VoteNet and its variants, eith…
CSDN: Cross-modal Shape-transfer Dual-refinement Network for Point Cloud Completion
Zhe Zhu, Liangliang Nan, Haoran Xie +4
How will you repair a physical object with some missings? You may imagine its original shape from previously captured images, recover its overall (global) but coarse shape first, a…
DDL-MVS: Depth Discontinuity Learning for MVS Networks
Nail Ibrahimli, Hugo Ledoux, Julian Kooij +1
Traditional MVS methods have good accuracy but struggle with completeness, while recently developed learning-based multi-view stereo (MVS) techniques have improved completeness exc…
HRBF-Fusion: Accurate 3D reconstruction from RGB-D data using on-the-fly implicits
Yabin Xu, Liangliang Nan, Laishui Zhou +2
Reconstruction of high-fidelity 3D objects or scenes is a fundamental research problem. Recent advances in RGB-D fusion have demonstrated the potential of producing 3D models from…
PSSNet: Planarity-sensible Semantic Segmentation of Large-scale Urban Meshes
Weixiao Gao, Liangliang Nan, Bas Boom +1
We introduce a novel deep learning-based framework to interpret 3D urban scenes represented as textured meshes. Based on the observation that object boundaries typically align with…