82 citations · 373 across the 16 of their papers we have counts for
16 papers
SUM Parts: Benchmarking Part-Level Semantic Segmentation of Urban Meshes
Weixiao Gao, Liangliang Nan, Hugo Ledoux
Semantic segmentation in urban scene analysis has mainly focused on images or point clouds, while textured meshes - offering richer spatial representation - remain underexplored. T…
Parametric Point Cloud Completion for Polygonal Surface Reconstruction
Zhaiyu Chen, Yuqing Wang, Liangliang Nan +1
Existing polygonal surface reconstruction methods heavily depend on input completeness and struggle with incomplete point clouds. We argue that while current point cloud completion…
PolyGNN: Polyhedron-based Graph Neural Network for 3D Building Reconstruction from Point Clouds
Zhaiyu Chen, Yilei Shi, Liangliang Nan +2
We present PolyGNN, a polyhedron-based graph neural network for 3D building reconstruction from point clouds. PolyGNN learns to assemble primitives obtained by polyhedral decomposi…
CoPR: Towards Accurate Visual Localization With Continuous Place-descriptor Regression
Mubariz Zaffar, Liangliang Nan, Julian Francisco Pieter Kooij
Visual Place Recognition (VPR) is an image-based localization method that estimates the camera location of a query image by retrieving the most similar reference image from a map o…
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…
Reconstructing Compact Building Models from Point Clouds Using Deep Implicit Fields
Zhaiyu Chen, Hugo Ledoux, Seyran Khademi +1
While three-dimensional (3D) building models play an increasingly pivotal role in many real-world applications, obtaining a compact representation of buildings remains an open prob…
GlobalMatch: Registration of Forest Terrestrial Point Clouds by Global Matching of Relative Stem Positions
Xufei Wang, Zexin Yang, Xiaojun Cheng +4
Registering point clouds of forest environments is an essential prerequisite for LiDAR applications in precision forestry. State-of-the-art methods for forest point cloud registrat…
3D Instance Segmentation of MVS Buildings
Jiazhou Chen, Yanghui Xu, Shufang Lu +2
We present a novel 3D instance segmentation framework for Multi-View Stereo (MVS) buildings in urban scenes. Unlike existing works focusing on semantic segmentation of urban scenes…
SUM: A Benchmark Dataset of Semantic Urban Meshes
Weixiao Gao, Liangliang Nan, Bas Boom +1
Recent developments in data acquisition technology allow us to collect 3D texture meshes quickly. Those can help us understand and analyse the urban environment, and as a consequen…
An End-to-End Geometric Deficiency Elimination Algorithm for 3D Meshes
Bingtao Ma, Hongsen Liu, Liangliang Nan +1
The 3D mesh is an important representation of geometric data. In the generation of mesh data, geometric deficiencies (e.g., duplicate elements, degenerate faces, isolated vertices,…
Curve Networks for Surface Reconstruction
Yuanhao Cao, Liangliang Nan, Peter Wonka
Man-made objects usually exhibit descriptive curved features (i.e., curve networks). The curve network of an object conveys its high-level geometric and topological structure. We p…