no code implementations • 23 Apr 2024 • Guangpeng Fan, Fei Yan, Xiangquan Zeng, Qingtao Xu, Ruoyoulan Wang, Binghong Zhang, Jialing Zhou, Liangliang Nan, Jinhu Wang, Zhiwei Zhang, Jia Wang
We proposed a method to map the canopy height of the primeval forest within the world-level giant tree distribution area by using a spaceborne LiDAR fusion satellite imagery (Global Ecosystem Dynamics Investigation (GEDI), ICESat-2, and Sentinel-2) driven deep learning modeling.
no code implementations • 31 Mar 2024 • Mubariz Zaffar, Liangliang Nan, Julian F. P. Kooij
In Visual Place Recognition (VPR) the pose of a query image is estimated by comparing the image to a map of reference images with known reference poses.
no code implementations • 20 Dec 2023 • Lipeng Gu, Xuefeng Yan, Liangliang Nan, Dingkun Zhu, Honghua Chen, Weiming Wang, Mingqiang Wei
The DSE module, designed for real-world autonomous driving scenarios, enhances the semantic perception of point clouds, particularly for distant points.
no code implementations • 8 Dec 2023 • Xin Li, Peng Li, Zeyong Wei, Zhe Zhu, Mingqiang Wei, Junhui Hou, Liangliang Nan, Jing Qin, Haoran Xie, Fu Lee Wang
By performing cross-modal interaction, Cross-BERT can smoothly reconstruct the masked tokens during pretraining, leading to notable performance enhancements for downstream tasks.
1 code implementation • 8 Dec 2023 • Nail Ibrahimli, Julian F. P. Kooij, Liangliang Nan
We introduce MuVieCAST, a modular multi-view consistent style transfer network architecture that enables consistent style transfer between multiple viewpoints of the same scene.
1 code implementation • 25 Sep 2023 • Shiming Wang, Holger Caesar, Liangliang Nan, Julian F. P. Kooij
To validate its effectiveness, we compare UniBEV to state-of-the-art BEVFusion and MetaBEV on nuScenes over all sensor input combinations.
1 code implementation • 17 Jul 2023 • Zhaiyu Chen, Yilei Shi, Liangliang Nan, Zhitong Xiong, Xiao Xiang Zhu
We present PolyGNN, a polyhedron-based graph neural network for 3D building reconstruction from point clouds.
no code implementations • 14 Apr 2023 • Mubariz Zaffar, Liangliang Nan, Julian Francisco Pieter Kooij
Firstly, the reference images for VPR are only available at sparse poses in a map, which enforces an upper bound on the maximum achievable localization accuracy through VPR.
1 code implementation • 23 Dec 2022 • Shenglan Du, Nail Ibrahimli, Jantien Stoter, Julian Kooij, Liangliang Nan
To improve the segmentation near object boundaries, we propose a boundary-aware feature propagation mechanism.
no code implementations • 31 Aug 2022 • Baian Chen, Liangliang Nan, Haoran Xie, Dening Lu, Fu Lee Wang, Mingqiang Wei
Capturing both local and global features of irregular point clouds is essential to 3D object detection (3OD).
no code implementations • 1 Aug 2022 • Zhe Zhu, Liangliang Nan, Haoran Xie, Honghua Chen, Mingqiang Wei, Jun Wang, Jing Qin
The first module transfers the intrinsic shape characteristics from single images to guide the geometry generation of the missing regions of point clouds, in which we propose IPAdaIN to embed the global features of both the image and the partial point cloud into completion.
Ranked #2 on Point Cloud Completion on ShapeNet-ViPC
1 code implementation • 2 Mar 2022 • Nail Ibrahimli, Hugo Ledoux, Julian Kooij, Liangliang Nan
We validate our idea and demonstrate that our strategies can be easily integrated into the existing learning-based MVS pipeline where the reconstruction depends on high-quality depth map estimation.
1 code implementation • 7 Feb 2022 • Weixiao Gao, Liangliang Nan, Bas Boom, Hugo Ledoux
The over-segmentation step generates an initial set of mesh segments that capture the planar and non-planar regions of urban scenes.
1 code implementation • 3 Feb 2022 • Yabin Xu, Liangliang Nan, Laishui Zhou, Jun Wang, Charlie C. L. Wang
However, due to the discrete nature and limited resolution of their surface representations (e. g., point- or voxel-based), existing approaches suffer from the accumulation of errors in camera tracking and distortion in the reconstruction, which leads to an unsatisfactory 3D reconstruction.
1 code implementation • 25 Jan 2022 • Jin Huang, Jantien Stoter, Ravi Peters, Liangliang Nan
A major challenge of urban reconstruction from airborne LiDAR point clouds lies in that the vertical walls are typically missing.
1 code implementation • 24 Dec 2021 • Zhaiyu Chen, Hugo Ledoux, Seyran Khademi, Liangliang Nan
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 problem.
1 code implementation • 21 Dec 2021 • Xufei Wang, Zexin Yang, Xiaojun Cheng, Jantien Stoter, Wenbing Xu, Zhenlun Wu, Liangliang Nan
Registering point clouds of forest environments is an essential prerequisite for LiDAR applications in precision forestry.
no code implementations • 18 Dec 2021 • Jiazhou Chen, Yanghui Xu, Shufang Lu, Ronghua Liang, Liangliang Nan
Based on these global masks, 3D roof instances are segmented out by mask back-projections and extended to the entire building instances through a Markov random field optimization.
2 code implementations • 27 Feb 2021 • Weixiao Gao, Liangliang Nan, Bas Boom, Hugo Ledoux
The contributions of this work are threefold: (1) a new benchmark dataset of semantic urban meshes, (2) a novel semi-automatic annotation framework, and (3) an annotation tool for 3D meshes.
no code implementations • 14 Mar 2020 • Bingtao Ma, Hongsen Liu, Liangliang Nan, Yang Cong
The 3D mesh is an important representation of geometric data.
no code implementations • ICCV 2017 • Liangliang Nan, Peter Wonka
We show that reconstruction from point clouds can be cast as a binary labeling problem.