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Related Experiment Video

Updated: May 9, 2026

Measuring Spatially- and Directionally-varying Light Scattering from Biological Material

Measuring Spatially- and Directionally-varying Light Scattering from Biological Material

Published on: May 20, 2013

Range image registration using a photometric metric under unknown lighting.

Diego Thomas¹, Akihiro Sugimoto

¹National Institute of Informatics, 2-1-2 Hitotsubashi, Chiyoda-ku, Tokyo, Japan. diego_thomas@nii.ac.jp

IEEE Transactions on Pattern Analysis and Machine Intelligence

|July 23, 2013

Summary

This summary is machine-generated.

This study introduces a new photometric metric for precise range image registration under challenging, unknown lighting conditions. The method accurately aligns 3D data by minimizing photometric reprojection error, proving effective in experiments.

Related Experiment Videos

Last Updated: May 9, 2026

Measuring Spatially- and Directionally-varying Light Scattering from Biological Material

Measuring Spatially- and Directionally-varying Light Scattering from Biological Material

Published on: May 20, 2013

Area of Science:

Computer Vision
3D Reconstruction
Photogrammetry

Background:

Accurate registration of range images is crucial for 3D reconstruction.
Existing methods struggle with unknown, general illumination and lack of distinct geometric features.
Photometric information is often underutilized in range image alignment.

Purpose of the Study:

To develop a novel photometric metric for evaluating rigid transformations between overlapping range images.
To enable accurate registration of range images captured under unknown and general lighting conditions.
To provide a practical method for registering range images lacking salient geometric features.

Main Methods:

Derivation of a photometric metric based on spherical harmonics representation of image formation.
Estimation of illumination and albedo values from point correspondences.
Computation of photometric reprojection error by synthesizing image colors.
Hypothesize-and-test strategy using spherical representation for transformation candidate generation.

Main Results:

A new photometric metric effectively evaluates rigid transformations for range image alignment.
The proposed method accurately registers range images under unknown, general illumination.
Experimental results validate the metric's usefulness on both synthetic and real data.

Conclusions:

The developed photometric metric offers a robust solution for range image registration challenges.
The method is particularly valuable for datasets with minimal geometric features and complex lighting.
This work advances the field of 3D data alignment and reconstruction.