The shading isophotes: Model and methods for Lambertian planes and a point light

IF 4.3 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Damien Mariyanayagam, Adrien Bartoli
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引用次数: 0

Abstract

Structure-from-Motion (SfM) and Shape-from-Shading (SfS) are complementary classical approaches to 3D vision. Broadly speaking, SfM exploits geometric primitives from textured surfaces and SfS exploits pixel intensity from the shading image. We propose an approach that exploits virtual geometric primitives extracted from the shading image, namely the level-sets, which we name shading isophotes. Our approach thus combines the strength of geometric reasoning with the rich shading information. We focus on the case of untextured Lambertian planes of unknown albedo lit by an unknown Point Light Source (PLS) of unknown intensity. We derive a comprehensive geometric model showing that the unknown scene parameters are in general all recoverable from a single image of at least two planes. We propose computational methods to detect the isophotes, to reconstruct the scene parameters in closed-form and to refine the results densely using pixel intensity. Our methods thus estimate light source, plane pose and camera pose parameters for untextured planes, which cannot be achieved by the existing approaches. We evaluate our model and methods on synthetic and real images.

阴影等透镜:朗伯平面和点光源的模型和方法
从运动看结构(SfM)和从阴影看形状(SfS)是互补的三维视觉经典方法。从广义上讲,SfM 利用纹理表面的几何基元,而 SfS 则利用阴影图像的像素强度。我们提出的方法是利用从阴影图像中提取的虚拟几何基元,即水平集,我们将其命名为阴影等值线。因此,我们的方法结合了几何推理的优势和丰富的阴影信息。我们将重点放在未知反照率的无纹理朗伯平面上,该平面由未知强度的未知点光源 (PLS) 照亮。我们推导出一个全面的几何模型,表明未知场景参数一般都可以从至少两个平面的单一图像中恢复。我们提出了检测等光点的计算方法,以闭合形式重建场景参数,并利用像素强度对结果进行密集细化。因此,我们的方法可以估算出无纹理平面的光源、平面姿态和相机姿态参数,而现有方法无法实现这一点。我们在合成图像和真实图像上评估了我们的模型和方法。
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来源期刊
Computer Vision and Image Understanding
Computer Vision and Image Understanding 工程技术-工程:电子与电气
CiteScore
7.80
自引率
4.40%
发文量
112
审稿时长
79 days
期刊介绍: The central focus of this journal is the computer analysis of pictorial information. Computer Vision and Image Understanding publishes papers covering all aspects of image analysis from the low-level, iconic processes of early vision to the high-level, symbolic processes of recognition and interpretation. A wide range of topics in the image understanding area is covered, including papers offering insights that differ from predominant views. Research Areas Include: • Theory • Early vision • Data structures and representations • Shape • Range • Motion • Matching and recognition • Architecture and languages • Vision systems
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