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Related Experiment Video

Updated: May 8, 2026

In Vivo Quantification of Hip Arthrokinematics during Dynamic Weight-bearing Activities using Dual Fluoroscopy

In Vivo Quantification of Hip Arthrokinematics during Dynamic Weight-bearing Activities using Dual Fluoroscopy

Published on: July 2, 2021

Learning Spatially-Smooth Mappings in Non-Rigid Structure from Motion.

Onur C Hamsici¹, Paulo F U Gotardo, Aleix M Martinez

¹Qualcomm Research, San Diego, CA, USA.

Computer Vision - ECCV ... : ... European Conference on Computer Vision : Proceedings. European Conference on Computer Vision

|August 16, 2013

Summary

This study introduces a novel non-rigid structure from motion (NRSFM) method using spatial smoothness instead of temporal smoothness. This approach effectively recovers 3D shapes from 2D data, even with abrupt deformations or missing temporal order.

Related Experiment Videos

Last Updated: May 8, 2026

In Vivo Quantification of Hip Arthrokinematics during Dynamic Weight-bearing Activities using Dual Fluoroscopy

In Vivo Quantification of Hip Arthrokinematics during Dynamic Weight-bearing Activities using Dual Fluoroscopy

Published on: July 2, 2021

Area of Science:

Computer Vision
3D Reconstruction
Geometric Modeling

Background:

Non-rigid structure from motion (NRSFM) is an underconstrained problem in computer vision.
Temporal smoothness constraints are commonly used but have limitations with unordered or abruptly deforming data.

Purpose of the Study:

To develop a new NRSFM method that overcomes limitations of temporal smoothness constraints.
To enable accurate 3D shape recovery from 2D data, irrespective of temporal ordering or deformation type.

Main Methods:

Deformations are modeled as spatial variations in shape space.
Spatial smoothness is enforced by learning a kernel-based mapping from 2D to 3D shape coefficients.
A rotation-invariant kernel is used to intrinsically define spatial smoothness in the feature space.

Main Results:

The method effectively handles NRSFM problems lacking temporal ordering.
Abrupt deformations are accommodated without compromising accuracy.
Compact shape variation representation is achieved using custom-learned coefficient bases.

Conclusions:

The proposed spatial smoothness approach offers a robust alternative to temporal smoothness for NRSFM.
The learned kernel-based mapping allows for efficient 3D shape recovery from new 2D observations.
This method advances the field of 3D reconstruction from motion.