The unknown spatial quality of dense point clouds derived from stereo images
1. The Unknown Spatial Quality of Dense Point Clouds Derived From
Stereo Images
Abstract:
Is it possible to use stereo images to generate point clouds and to compute
rigorous uncertainty maps? Currently, neither modern commercial
photogrammetric software nor state-of-the-art algorithms are able to
provide a spatial distribution of uncertainty. In this letter, we explain why
this is the case, despite a high demand from the user community. Many
applications would indeed benefit from the availability of error bars on
each point, as uncertainties on derived models and quantities could be
accurately predicted. For instance, change detection could be performed
rigorously since the statistical significance of observed changes could be
computed. In this letter, we focus on dense stereo methods. We first
explain that it is not possible to derive reliable predictive uncertainties
mainly due to matching and modeling errors. Our research shows that
both intrinsic and practical limitations of the algorithms lead to
unpredictable artifacts. Then, we focus on the use of empirical errors,
showing that, despite the redundancy brought by multiview stereo, there is
a fundamental limitation due to the unknown density of independent
measurements. We think that these problems will represent a big challenge
2. for the future, as these limitations cannot be addressed by algorithmic
design, computational power, or imaging sensor technology.
Existing System:
Some users of 3-D data wish to have estimates of the accuracy for each
point, or each elevation in a gridded digital surface model (DSM). Usually,
one assumes a spatially uniform error model and performs an assessment
using reference data sets or ground control points (GCPs). It would allow
them to put an error bar on any inferred physical quantity, which is of
crucial importance in flood hazard modeling, or change detection, for
instance. For flood applications, we need to know the probability of a given
elevation to exceed a given threshold.
Proposed System:
One of the main issues with stereo algorithms is the presence of gross
errors (a few pixels at least), which are not simply fluctuations due to noise
but the result of matching failures. A good illustration is provided,
outlining some dramatic differences between various state-of-the-art
algorithms.
Hardware Requirements:
• System : Pentium IV 2.4 GHz.
3. • Hard Disk : 40 GB.
• Floppy Drive : 1.44 Mb.
• Monitor : 15 VGA Colour.
• Mouse : Logitech.
• RAM : 256 Mb.
Software Requirements:
• Operating system : - Windows XP.
• Front End : - JSP
• Back End : - SQL Server
Software Requirements:
• Operating system : - Windows XP.
• Front End : - .Net
• Back End : - SQL Server
