Shading-based high-quality 3D object reconstruction
3D object reconstruction is the process of reconstructing real objects into the digital world. Reconstructing the shape of a 3D object from multi-view images under unknown, general illumination is a fundamental problem in computer vision. Extensive research has been done in this area and many te...
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Format: | Theses and Dissertations |
Language: | English |
Published: |
2016
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Online Access: | http://hdl.handle.net/10356/66339 |
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Institution: | Nanyang Technological University |
Language: | English |
Summary: | 3D object reconstruction is the process of reconstructing real
objects into the digital world. Reconstructing the shape of a 3D
object from multi-view images under unknown, general illumination is
a fundamental problem in computer vision. Extensive research has
been done in this area and many techniques have been developed.
Though the state-of-the-art has achieved great success, many methods
still have various underlying requirements or heavy assumptions that
limit their application scope in practice. This thesis investigates
both the shape refinement and its related lighting recovery. We
approach the problem with different focuses: quality, robustness and
efficiency. Our goal is to design and develop effective algorithms
that solve the long-lasting problems in 3D reconstruction.
Firstly, we consider the problem of high-quality 3D reconstruction
under unknown illumination. No assumption on object albedos makes
the problem challenging, especially when recovering surface details.
We present a total variation (TV) based approach for recovering
surface details using shading and multi-view stereo (MVS), with the
lighting modeled as overall illumination vectors. Behind the
approach are our two important observations: (1) the illumination
over the surface of an object tends to be piecewise smooth and (2)
the recovery of surface orientation is not sufficient for
reconstructing geometry, which were previously overlooked. Thus we
introduce TV to regularize the lighting and use visual hull to
constrain partial vertices. The reconstruction is formulated as a
constrained TV-minimization problem that treats the shape and
lighting as unknowns simultaneously. An augmented Lagrangian method
is proposed to quickly solve the TV-minimization problem. Our
approach recovers high quality of surface details even starting with
a coarse MVS.
Secondly, considering that the existing Shape-from-shading methods
usually assume Lambertian surfaces, we extend the algorithm to make
it robust to non-Lambertian surfaces as well. Based on the
independence property of diffuse reflectance and specular
reflectance, we introduce the specular intensity variable and tackle
the two types of reflectance separately. Different from existing
works, the proposed algorithm requires no prior knowledge or
hardware setups and only has the assumption that the light sources
are fixed and distant. By iteratively solving the lighting, specular
intensity and geometry, the extended framework effectively deals
with highlight effects which cannot be solved by traditional
methods. Even for the challenging non-Lambertian object, our
algorithm is able to remove the highlight and recover its surface
details robustly.
Thirdly, to improve the efficiency of current work, we propose a
novel mesh refinement framework by optimizing the face normals
instead of vertex normals. The traditional vertex-based methods
usually have high computational cost and thus suffer from problems
like long processing time and low density output. Our proposed
framework focuses on the mesh face normals and remove the
complicated non-linear computations of traditional methods. As a
result, the maximum size of the mesh that can be handled by our
framework is substantially improved. As the denser meshes are
usually favored in common evaluation criteria, results generated by
the proposed framework have showed improved performance with greatly
reduced runtime. |
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