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. 2001.07904
  4. Cited By
Dynamic multi-object Gaussian process models: A framework for
  data-driven functional modelling of human joints

Dynamic multi-object Gaussian process models: A framework for data-driven functional modelling of human joints

22 January 2020
Jean-Rassaire Fouefack
Bhushan S Borotikar
T. Douglas
Valérie Burdin
Tinashe Ernest Mutsvangwa
ArXivPDFHTML

Papers citing "Dynamic multi-object Gaussian process models: A framework for data-driven functional modelling of human joints"

2 / 2 papers shown
Title
MIRACLE3D: Memory-efficient Integrated Robust Approach for Continual Learning on Point Clouds via Shape Model Construction
MIRACLE3D: Memory-efficient Integrated Robust Approach for Continual Learning on Point Clouds via Shape Model Construction
Hossein Resani
B. Nasihatkon
3DV
195
0
0
08 Oct 2024
Approximating Intersections and Differences Between Linear Statistical
  Shape Models Using Markov Chain Monte Carlo
Approximating Intersections and Differences Between Linear Statistical Shape Models Using Markov Chain Monte Carlo
Maximilian Weiherer
Finn Klein
Bernhard Egger
22
2
0
29 Nov 2022
1