Illumination Estimation Method Based on Linear Transformation of Cosine Spherical Distribution

doi:10.12068/j.issn.1005-3026.2025.20240030

Abstract

Abstract:

To accurately describe and parameterize scene light sources and achieve high-precision single-image illumination estimation， an illumination representation method based on linear transformation of cosine spherical distribution was proposed. A regression neural network was designed to infer the parametric distribution and intensity of light sources from a single image. A loss function based on singular value decomposition was innovatively introduced. This function could precisely and succinctly measure the distance between two parameterized light sources， significantly enhancing the accuracy of the regression network. Experimental results demonstrate that，compared with existing methods， this method performs exceptionally well under complex illumination conditions， particularly showing a notable improvement in capturing anisotropic illumination information.

Key words: illumination estimation, linear transformation, cosine spherical distribution, regression network, singular value decomposition

CLC Number:

TP 391.4

Lian-jiang YU, Hong-juan LIU. Illumination Estimation Method Based on Linear Transformation of Cosine Spherical Distribution[J]. Journal of Northeastern University(Natural Science), 2025, 46(9): 34-40.

Figures/Tables 7

Fig.1 Framework of LTCLight illumination estimation method based on linear transformation of cosine spherical distribution

Fig.2 Various high-frequency illumination distributions controlled by linear transformation matrix

Fig.3 Schematic diagram of SVD decomposition, rotation and scaling of linear transformation represented by matrix M

Table 1 Comparison of errors between LTC illumination representation, SG and SH methods

评估指标	SG(196个锚点)			SH(10阶)			LTC(10个光源)
评估指标	DM	SM	MM	DM	SM	MM	DM	SM	MM
RSME	6.125	6.527	6.752	7.087	7.248	7.301	5.789	5.871	6.097
si-RSME	0.464	0.445	1.219	0.310	0.360	0.696	0.207	0.258	0.519
AE/（°）	12.26	12.83	25.12	12.79	14.72	20.94	9.01	9.65	15.14

Fig.4 Comparison of real illumination and light sources generated by LTC, SG and SH methods

Table 2 Comparison of errors between LTCLight, EMLight regression network and Gardner 2019 method

评估指标	EMLight的回归网络			Gardner 2019			LTCLight
评估指标	DM	SM	MM	DM	SM	MM	DM	SM	MM
RSME	7.089	7.049	6.970	7.155	7.147	6.822	6.971	6.841	6.528
si-RSME	0.373	0.353	1.019	0.424	0.456	1.257	0.309	0.310	0.615
AE/（°）	15.32	14.26	25.78	16.62	16.91	31.48	13.06	12.98	18.91

Fig.5 Comparison of regression parameterized light sources between real illumination and EMLight regression network, Gardner 2019 and LTCLight method

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