# An Effective Approach for NRSFM of Small-Size Image Sequences

## TL;DR

This paper introduces a new method for 3D shape estimation from small image sequences, improving accuracy and robustness in computer vision.

## Contribution

A sub-sequence-based integrated algorithm is proposed to enhance NRSFM performance for small image sequences.

## Key findings

- The proposed method achieves higher estimation accuracy than existing approaches.
- Trimmed mean computation improves robustness by reducing errors from weaker estimators.

## Abstract

In recent years, non-rigid structure from motion (NRSFM) has become one of the hottest issues in computer vision due to its wide applications. In practice, the number of available high-quality images may be limited in many cases. Under such a condition, the performances may not be satisfactory when existing NRSFM algorithms are applied directly to estimate the 3D coordinates of a small-size image sequence. In this paper, a sub-sequence-based integrated algorithm is proposed to deal with the NRSFM problem with small sequence sizes. In the proposed method, sub-sequences are first extracted from the original sequence. In order to obtain diversified estimations, multiple weaker estimators are constructed by applying the extracted sub-sequences to a recent NRSFM algorithm with a rotation-invariant kernel (RIK). Compared to other first-order statistics, the trimmed mean is a relatively robust statistic. Considering the fact that the estimations of some weaker estimators may have large errors, the trimmed means of the outputs for all the weaker estimators are computed to determine the final estimated 3D shapes. Compared to some existing methods, the proposed algorithm can achieve a higher estimation accuracy, and has better robustness. Experimental results on several widely used image sequences demonstrate the effectiveness and feasibility of the proposed algorithm.