ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 2012.04708
  4. Cited By
ODFNet: Using orientation distribution functions to characterize 3D
  point clouds

ODFNet: Using orientation distribution functions to characterize 3D point clouds

8 December 2020
Y. Sahin
Alican Mertan
Gözde B. Ünal
    3DPC
ArXivPDFHTML

Papers citing "ODFNet: Using orientation distribution functions to characterize 3D point clouds"

4 / 4 papers shown
Title
A comprehensive overview of deep learning techniques for 3D point cloud
  classification and semantic segmentation
A comprehensive overview of deep learning techniques for 3D point cloud classification and semantic segmentation
Sushmita Sarker
Prithul Sarker
Gunner Stone
Ryan Gorman
Alireza Tavakkoli
G. Bebis
Javad Sattarvand
3DPC
3DV
24
12
0
20 May 2024
Symmetry and Variance: Generative Parametric Modelling of Historical
  Brick Wall Patterns
Symmetry and Variance: Generative Parametric Modelling of Historical Brick Wall Patterns
Sevgi Altun
Mustafa Cem Gunes
Y. Sahin
Alican Mertan
Gözde B. Ünal
M. Özkar
17
0
0
23 Oct 2022
DensePoint: Learning Densely Contextual Representation for Efficient
  Point Cloud Processing
DensePoint: Learning Densely Contextual Representation for Efficient Point Cloud Processing
Yongcheng Liu
Bin Fan
Gaofeng Meng
Jiwen Lu
Shiming Xiang
Chunhong Pan
3DPC
119
270
0
09 Sep 2019
SpiderCNN: Deep Learning on Point Sets with Parameterized Convolutional
  Filters
SpiderCNN: Deep Learning on Point Sets with Parameterized Convolutional Filters
Yifan Xu
Tianqi Fan
Mingye Xu
Long Zeng
Yu Qiao
3DV
3DPC
150
768
0
30 Mar 2018
1