178 research outputs found
Linear Differential Constraints for Photo-polarimetric Height Estimation
In this paper we present a differential approach to photo-polarimetric shape
estimation. We propose several alternative differential constraints based on
polarisation and photometric shading information and show how to express them
in a unified partial differential system. Our method uses the image ratios
technique to combine shading and polarisation information in order to directly
reconstruct surface height, without first computing surface normal vectors.
Moreover, we are able to remove the non-linearities so that the problem reduces
to solving a linear differential problem. We also introduce a new method for
estimating a polarisation image from multichannel data and, finally, we show it
is possible to estimate the illumination directions in a two source setup,
extending the method into an uncalibrated scenario. From a numerical point of
view, we use a least-squares formulation of the discrete version of the
problem. To the best of our knowledge, this is the first work to consider a
unified differential approach to solve photo-polarimetric shape estimation
directly for height. Numerical results on synthetic and real-world data confirm
the effectiveness of our proposed method.Comment: To appear at International Conference on Computer Vision (ICCV),
Venice, Italy, October 22-29, 201
Cross-calibration of Time-of-flight and Colour Cameras
Time-of-flight cameras provide depth information, which is complementary to
the photometric appearance of the scene in ordinary images. It is desirable to
merge the depth and colour information, in order to obtain a coherent scene
representation. However, the individual cameras will have different viewpoints,
resolutions and fields of view, which means that they must be mutually
calibrated. This paper presents a geometric framework for this multi-view and
multi-modal calibration problem. It is shown that three-dimensional projective
transformations can be used to align depth and parallax-based representations
of the scene, with or without Euclidean reconstruction. A new evaluation
procedure is also developed; this allows the reprojection error to be
decomposed into calibration and sensor-dependent components. The complete
approach is demonstrated on a network of three time-of-flight and six colour
cameras. The applications of such a system, to a range of automatic
scene-interpretation problems, are discussed.Comment: 18 pages, 12 figures, 3 table
A Neural Height-Map Approach for the Binocular Photometric Stereo Problem
In this work we propose a novel, highly practical, binocular photometric
stereo (PS) framework, which has same acquisition speed as single view PS,
however significantly improves the quality of the estimated geometry.
As in recent neural multi-view shape estimation frameworks such as NeRF,
SIREN and inverse graphics approaches to multi-view photometric stereo (e.g.
PS-NeRF) we formulate shape estimation task as learning of a differentiable
surface and texture representation by minimising surface normal discrepancy for
normals estimated from multiple varying light images for two views as well as
discrepancy between rendered surface intensity and observed images. Our method
differs from typical multi-view shape estimation approaches in two key ways.
First, our surface is represented not as a volume but as a neural heightmap
where heights of points on a surface are computed by a deep neural network.
Second, instead of predicting an average intensity as PS-NeRF or introducing
lambertian material assumptions as Guo et al., we use a learnt BRDF and perform
near-field per point intensity rendering.
Our method achieves the state-of-the-art performance on the DiLiGenT-MV
dataset adapted to binocular stereo setup as well as a new binocular
photometric stereo dataset - LUCES-ST.Comment: WACV 202
Low-cost single-pixel 3D imaging by using an LED array
We propose a method to perform color imaging with a single photodiode by using
light structured illumination generated with a low-cost color LED array. The LED array is
used to generate a sequence of color Hadamard patterns which are projected onto the object
by a simple optical system while the photodiode records the light intensity. A field
programmable gate array (FPGA) controls the LED panel allowing us to obtain high refresh
rates up to 10 kHz. The system is extended to 3D imaging by simply adding a low number of
photodiodes at different locations. The 3D shape of the object is obtained by using a noncalibrated
photometric stereo technique. Experimental results are provided for an LED array
with 32 × 32 elements
Ear-to-ear Capture of Facial Intrinsics
We present a practical approach to capturing ear-to-ear face models
comprising both 3D meshes and intrinsic textures (i.e. diffuse and specular
albedo). Our approach is a hybrid of geometric and photometric methods and
requires no geometric calibration. Photometric measurements made in a
lightstage are used to estimate view dependent high resolution normal maps. We
overcome the problem of having a single photometric viewpoint by capturing in
multiple poses. We use uncalibrated multiview stereo to estimate a coarse base
mesh to which the photometric views are registered. We propose a novel approach
to robustly stitching surface normal and intrinsic texture data into a
seamless, complete and highly detailed face model. The resulting relightable
models provide photorealistic renderings in any view
An improved photometric stereo through distance estimation and light vector optimization from diffused maxima region
© 2013 Elsevier B.V. All rights reserved. Although photometric stereo offers an attractive technique for acquiring 3D data using low-cost equipment, inherent limitations in the methodology have served to limit its practical application, particularly in measurement or metrology tasks. Here we address this issue. Traditional photometric stereo assumes that lighting directions at every pixel are the same, which is not usually the case in real applications, and especially where the size of object being observed is comparable to the working distance. Such imperfections of the illumination may make the subsequent reconstruction procedures used to obtain the 3D shape of the scene prone to low frequency geometric distortion and systematic error (bias). Also, the 3D reconstruction of the object results in a geometric shape with an unknown scale. To overcome these problems a novel method of estimating the distance of the object from the camera is developed, which employs photometric stereo images without using other additional imaging modality. The method firstly identifies Lambertian diffused maxima region to calculate the object distance from the camera, from which the corrected per-pixel light vector is able to be derived and the absolute dimensions of the object can be subsequently estimated. We also propose a new calibration process to allow a dynamic(as an object moves in the field of view) calculation of light vectors for each pixel with little additional computation cost. Experiments performed on synthetic as well as real data demonstrates that the proposed approach offers improved performance, achieving a reduction in the estimated surface normal error of up to 45% as well as mean height error of reconstructed surface of up to 6 mm. In addition, when compared to traditional photometric stereo, the proposed method reduces the mean angular and height error so that it is low, constant and independent of the position of the object placement within a normal working range
Linear Differential Constraints for Photo-polarimetric Height Estimation
In this paper we present a differential approach to photopolarimetric shape estimation. We propose several alternative differential constraints based on polarisation and photometric shading information and show how to express them in a unified partial differential system. Our method uses the image ratios technique to combine shading and polarisation information in order to directly reconstruct surface height, without first computing surface normal vectors. Moreover, we are able to remove the non-linearities so that the problem reduces to solving a linear differential problem. We also introduce a new method for estimating a polarisation image from multichannel data and, finally, we show it is possible to estimate the illumination directions in a two source setup, extending the method into an uncalibrated scenario. From a numerical point of view, we use a least-squares formulation of the discrete version of the problem. To the best of our knowledge, this is the first work to consider a unified differential approach to solve photo-polarimetric shape estimation directly for height. Numerical results on synthetic and real-world data confirm the effectiveness of our proposed method
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