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Liangliang Nan

Delft University of Technology

16 papers hereh-index 252.3k citations56 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • middle author11
  • last author5

Across the 16 of 16 papers where every author was matched, so the position is known.

fields
  • cs.CV15
  • cs.GR1
affiliations
  • Delft University of Technology
HomepageORCID 0000-0002-5629-9975
same name
  • Liangliang Nan — 17 papers, h 6
  • Liangliang Nan — 4 papers, h 3
  • Liangliang Nan — 2 papers
  • Liangliang Nan — 1 paper, h 1

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20162025
most citedSUM: A Benchmark Dataset of Semantic Urban Meshes

82 citations · 373 across the 16 of their papers we have counts for

collaborators
Showing 2021Show all

4 papers · 1 filter

cs.CV2021★ 79 cited

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…

cs.CV2021★ 40 cited

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…

cs.CV2021★ 26 cited

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…

cs.CV2021★ 82 cited

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…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.