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Related Experiment Video

Updated: Jun 3, 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

Computing Smooth Time Trajectories for Camera and Deformable Shape in Structure from Motion with Occlusion.

Paulo F U Gotardo, Aleix M Martinez

IEEE Transactions on Pattern Analysis and Machine Intelligence

|March 9, 2011

Summary

This summary is machine-generated.

This study introduces efficient methods for structure from motion (SFM) using Discrete Cosine Transform (DCT) to handle missing data in computer vision. The approach improves 3D reconstruction for both rigid and nonrigid shapes, even with significant occlusions.

Related Experiment Videos

Last Updated: Jun 3, 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
Machine Learning

Background:

Classical structure from motion (SFM) methods struggle with occlusions and missing data.
Existing models lack robust handling of smooth 2D point trajectories and deformable 3D shapes over time.

Purpose of the Study:

To develop efficient and robust methods for rigid and nonrigid SFM, particularly in the presence of occlusions and missing data.
To improve 3D shape reconstruction accuracy and computational efficiency.

Main Methods:

Utilizing Discrete Cosine Transform (DCT) domain parameterizations to estimate the column space of observation matrices.
Developing new models for 2D point trajectories, affine/weak-perspective cameras, and 3D deformable shapes.
Implementing a novel 3D shape trajectory approach for nonrigid SFM, modeling deformation as a trajectory in a linear shape space.

Main Results:

Proposed methods demonstrate tolerance to high percentages of missing data in SFM.
Achieved direct Euclidean 3D shape reconstructions for rigid SFM without postprocessing.
Outperformed state-of-the-art algorithms in modeling complex articulated deformations for nonrigid SFM.

Conclusions:

The DCT-based approach offers efficient and robust solutions for SFM with occlusions and missing data.
The novel nonrigid SFM method effectively captures complex deformations while adhering to low-rank constraints.
The study provides a viable approach for nonrigid SFM even when observation data is incomplete.