75 research outputs found
Structure and motion estimation from apparent contours under circular motion
In this paper, we address the problem of recovering structure and motion from the apparent contours of a smooth surface. Fixed image features under circular motion and their relationships with the intrinsic parameters of the camera are exploited to provide a simple parameterization of the fundamental matrix relating any pair of views in the sequence. Such a parameterization allows a trivial initialization of the motion parameters, which all bear physical meaning. It also greatly reduces the dimension of the search space for the optimization problem, which can now be solved using only two epipolar tangents. In contrast to previous methods, the motion estimation algorithm introduced here can cope with incomplete circular motion and more widely spaced images. Existing techniques for model reconstruction from apparent contours are then reviewed and compared. Experiment on real data has been carried out and the 3D model reconstructed from the estimated motion is presented. © 2002 Elsevier Science B.V. All rights reserved.postprin
3D Dynamic Scene Reconstruction from Multi-View Image Sequences
A confirmation report outlining my PhD research plan is presented. The PhD research topic is 3D dynamic scene reconstruction from multiple view image sequences. Chapter 1 describes the motivation and research aims. An overview of the progress in the past year is included. Chapter 2 is a review of volumetric scene reconstruction techniques and Chapter 3 is an in-depth description of my proposed reconstruction method. The theory behind the proposed volumetric scene reconstruction method is also presented, including topics in projective geometry, camera calibration and energy minimization. Chapter 4 presents the research plan and outlines the future work planned for the next two years
A Survey of Methods for Volumetric Scene Reconstruction from Photographs
Scene reconstruction, the task of generating a 3D model of a scene given multiple 2D photographs taken of the scene, is an old and difficult problem in computer vision. Since its introduction, scene reconstruction has found application in many fields, including robotics, virtual reality, and entertainment. Volumetric models are a natural choice for scene reconstruction. Three broad classes of volumetric reconstruction techniques have been developed based on geometric intersections, color consistency, and pair-wise matching. Some of these techniques have spawned a number of variations and undergone considerable refinement. This paper is a survey of techniques for volumetric scene reconstruction
Robust surface modelling of visual hull from multiple silhouettes
Reconstructing depth information from images is one of the actively researched themes
in computer vision and its application involves most vision research areas from object
recognition to realistic visualisation. Amongst other useful vision-based reconstruction
techniques, this thesis extensively investigates the visual hull (VH) concept for volume
approximation and its robust surface modelling when various views of an object are
available. Assuming that multiple images are captured from a circular motion, projection
matrices are generally parameterised in terms of a rotation angle from a reference position
in order to facilitate the multi-camera calibration. However, this assumption is often
violated in practice, i.e., a pure rotation in a planar motion with accurate rotation angle
is hardly realisable. To address this problem, at first, this thesis proposes a calibration
method associated with the approximate circular motion.
With these modified projection matrices, a resulting VH is represented by a hierarchical
tree structure of voxels from which surfaces are extracted by the Marching
cubes (MC) algorithm. However, the surfaces may have unexpected artefacts caused by
a coarser volume reconstruction, the topological ambiguity of the MC algorithm, and
imperfect image processing or calibration result. To avoid this sensitivity, this thesis
proposes a robust surface construction algorithm which initially classifies local convex
regions from imperfect MC vertices and then aggregates local surfaces constructed by the
3D convex hull algorithm. Furthermore, this thesis also explores the use of wide baseline
images to refine a coarse VH using an affine invariant region descriptor. This improves
the quality of VH when a small number of initial views is given.
In conclusion, the proposed methods achieve a 3D model with enhanced accuracy.
Also, robust surface modelling is retained when silhouette images are degraded by
practical noise
Reconstruction of sculpture from its profiles with unknown camera positions
Profiles of a sculpture provide rich information about its geometry, and can be used for shape recovery under known camera motion. By exploiting correspondences induced by epipolar tangents on the profiles, a successful solution to motion estimation from profiles has been developed in the special case of circular motion. The main drawbacks of using circular motion alone, namely the difficulty in adding new views and part of the object always being invisible, can be overcome by incorporating arbitrary general views of the object and registering its new profiles with the set of profiles resulted from the circular motion. In this paper, we describe a complete and practical system for producing a three-dimensional (3-D) model from uncalibrated images of an arbitrary object using its profiles alone. Experimental results on various objects are presented, demonstrating the quality of the reconstructions using the estimated motion.published_or_final_versio
Robust recovery of shapes with unknown topology from the dual space
In this paper, we address the problem of reconstructing an object surface from silhouettes. Previous works by other authors have shown that, based on the principle of duality, surface points can be recovered, theoretically, as the dual to the tangent plane space of the object. In practice, however, the identification of tangent basis in the tangent plane space is not trivial given a set of discretely sampled data. This problem is further complicated by the existence of bi-tangents to the object surface. The key contribution of this paper is the introduction of epipolar parameterization in identifying a well-defined local tangent basis. This extends the applicability of existing dual space reconstruction methods to fairly complicated shapes, without making any explicit assumption on the object topology. We verify our approach with both synthetic and real-world data, and compare it both qualitatively and quantitatively with other popular reconstruction algorithms. Experimental results demonstrate that our proposed approach produces more accurate estimation, whilst maintaining reasonable robustness towards shapes with complex topologies. © 2007 IEEE.published_or_final_versio
Robust surface modelling of visual hull from multiple silhouettes
Reconstructing depth information from images is one of the actively researched themes
in computer vision and its application involves most vision research areas from object
recognition to realistic visualisation. Amongst other useful vision-based reconstruction
techniques, this thesis extensively investigates the visual hull (VH) concept for volume
approximation and its robust surface modelling when various views of an object are
available. Assuming that multiple images are captured from a circular motion, projection
matrices are generally parameterised in terms of a rotation angle from a reference position
in order to facilitate the multi-camera calibration. However, this assumption is often
violated in practice, i.e., a pure rotation in a planar motion with accurate rotation angle
is hardly realisable. To address this problem, at first, this thesis proposes a calibration
method associated with the approximate circular motion.
With these modified projection matrices, a resulting VH is represented by a hierarchical
tree structure of voxels from which surfaces are extracted by the Marching
cubes (MC) algorithm. However, the surfaces may have unexpected artefacts caused by
a coarser volume reconstruction, the topological ambiguity of the MC algorithm, and
imperfect image processing or calibration result. To avoid this sensitivity, this thesis
proposes a robust surface construction algorithm which initially classifies local convex
regions from imperfect MC vertices and then aggregates local surfaces constructed by the
3D convex hull algorithm. Furthermore, this thesis also explores the use of wide baseline
images to refine a coarse VH using an affine invariant region descriptor. This improves
the quality of VH when a small number of initial views is given.
In conclusion, the proposed methods achieve a 3D model with enhanced accuracy.
Also, robust surface modelling is retained when silhouette images are degraded by
practical noise
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