1,160 research outputs found
Polarimetric PatchMatch Multi-View Stereo
PatchMatch Multi-View Stereo (PatchMatch MVS) is one of the popular MVS
approaches, owing to its balanced accuracy and efficiency. In this paper, we
propose Polarimetric PatchMatch multi-view Stereo (PolarPMS), which is the
first method exploiting polarization cues to PatchMatch MVS. The key of
PatchMatch MVS is to generate depth and normal hypotheses, which form local 3D
planes and slanted stereo matching windows, and efficiently search for the best
hypothesis based on the consistency among multi-view images. In addition to
standard photometric consistency, our PolarPMS evaluates polarimetric
consistency to assess the validness of a depth and normal hypothesis, motivated
by the physical property that the polarimetric information is related to the
object's surface normal. Experimental results demonstrate that our PolarPMS can
improve the accuracy and the completeness of reconstructed 3D models,
especially for texture-less surfaces, compared with state-of-the-art PatchMatch
MVS methods
Polarimetric Multi-View Inverse Rendering
A polarization camera has great potential for 3D reconstruction since the
angle of polarization (AoP) of reflected light is related to an object's
surface normal. In this paper, we propose a novel 3D reconstruction method
called Polarimetric Multi-View Inverse Rendering (Polarimetric MVIR) that
effectively exploits geometric, photometric, and polarimetric cues extracted
from input multi-view color polarization images. We first estimate camera poses
and an initial 3D model by geometric reconstruction with a standard
structure-from-motion and multi-view stereo pipeline. We then refine the
initial model by optimizing photometric and polarimetric rendering errors using
multi-view RGB and AoP images, where we propose a novel polarimetric rendering
cost function that enables us to effectively constrain each estimated surface
vertex's normal while considering four possible ambiguous azimuth angles
revealed from the AoP measurement. Experimental results using both synthetic
and real data demonstrate that our Polarimetric MVIR can reconstruct a detailed
3D shape without assuming a specific polarized reflection depending on the
material.Comment: Paper accepted in ECCV 202
Polarimetric Multi-View Inverse Rendering
A polarization camera has great potential for 3D reconstruction since the
angle of polarization (AoP) and the degree of polarization (DoP) of reflected
light are related to an object's surface normal. In this paper, we propose a
novel 3D reconstruction method called Polarimetric Multi-View Inverse Rendering
(Polarimetric MVIR) that effectively exploits geometric, photometric, and
polarimetric cues extracted from input multi-view color-polarization images. We
first estimate camera poses and an initial 3D model by geometric reconstruction
with a standard structure-from-motion and multi-view stereo pipeline. We then
refine the initial model by optimizing photometric rendering errors and
polarimetric errors using multi-view RGB, AoP, and DoP images, where we propose
a novel polarimetric cost function that enables an effective constraint on the
estimated surface normal of each vertex, while considering four possible
ambiguous azimuth angles revealed from the AoP measurement. The weight for the
polarimetric cost is effectively determined based on the DoP measurement, which
is regarded as the reliability of polarimetric information. Experimental
results using both synthetic and real data demonstrate that our Polarimetric
MVIR can reconstruct a detailed 3D shape without assuming a specific surface
material and lighting condition.Comment: Paper accepted in IEEE Transactions on Pattern Analysis and Machine
Intelligence (2022). arXiv admin note: substantial text overlap with
arXiv:2007.0883
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
Deep learning in remote sensing: a review
Standing at the paradigm shift towards data-intensive science, machine
learning techniques are becoming increasingly important. In particular, as a
major breakthrough in the field, deep learning has proven as an extremely
powerful tool in many fields. Shall we embrace deep learning as the key to all?
Or, should we resist a 'black-box' solution? There are controversial opinions
in the remote sensing community. In this article, we analyze the challenges of
using deep learning for remote sensing data analysis, review the recent
advances, and provide resources to make deep learning in remote sensing
ridiculously simple to start with. More importantly, we advocate remote sensing
scientists to bring their expertise into deep learning, and use it as an
implicit general model to tackle unprecedented large-scale influential
challenges, such as climate change and urbanization.Comment: Accepted for publication IEEE Geoscience and Remote Sensing Magazin
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