3,290 research outputs found
Trifocal Relative Pose from Lines at Points and its Efficient Solution
We present a new minimal problem for relative pose estimation mixing point
features with lines incident at points observed in three views and its
efficient homotopy continuation solver. We demonstrate the generality of the
approach by analyzing and solving an additional problem with mixed point and
line correspondences in three views. The minimal problems include
correspondences of (i) three points and one line and (ii) three points and two
lines through two of the points which is reported and analyzed here for the
first time. These are difficult to solve, as they have 216 and - as shown here
- 312 solutions, but cover important practical situations when line and point
features appear together, e.g., in urban scenes or when observing curves. We
demonstrate that even such difficult problems can be solved robustly using a
suitable homotopy continuation technique and we provide an implementation
optimized for minimal problems that can be integrated into engineering
applications. Our simulated and real experiments demonstrate our solvers in the
camera geometry computation task in structure from motion. We show that new
solvers allow for reconstructing challenging scenes where the standard two-view
initialization of structure from motion fails.Comment: This material is based upon work supported by the National Science
Foundation under Grant No. DMS-1439786 while most authors were in residence
at Brown University's Institute for Computational and Experimental Research
in Mathematics -- ICERM, in Providence, R
(SI10-124) Inverse Reconstruction Methodologies: A Review
The three-dimensional reconstruction problem is a longstanding ill-posed problem, which has made enormous progress in the field of computer vision. This field has attracted increasing interest and demonstrated an impressive performance. Due to a long era of increasing evolution, this paper presents an extensive review of the developments made in this field. For the three dimensional visualization, researchers have focused on the developments of three dimensional information and acquisition methodologies from two dimensional scenes or objects. These acquisition methodologies require a complex calibration procedure which is not practical in general. Hence, the requirement of flexibility was much needed in all these methods. Due to this emerging factors, many techniques were presented. The methodologies are organized on the basis of different aspects of the three dimensional reconstruction like active method, passive method, different geometrical shapes, etc. A brief analysis and comparison of the performance of these methodologies are also presented
Distributed scene reconstruction from multiple mobile platforms
Recent research on mobile robotics has produced new designs that provide
house-hold robots with omnidirectional motion. The image sensor embedded
in these devices motivates the application of 3D vision techniques on them
for navigation and mapping purposes. In addition to this, distributed cheapsensing
systems acting as unitary entity have recently been discovered as an
efficient alternative to expensive mobile equipment.
In this work we present an implementation of a visual reconstruction method,
structure from motion (SfM), on a low-budget, omnidirectional mobile platform,
and extend this method to distributed 3D scene reconstruction with
several instances of such a platform.
Our approach overcomes the challenges yielded by the plaform. The unprecedented
levels of noise produced by the image compression typical of
the platform is processed by our feature filtering methods, which ensure
suitable feature matching populations for epipolar geometry estimation by
means of a strict quality-based feature selection. The robust pose estimation
algorithms implemented, along with a novel feature tracking system,
enable our incremental SfM approach to novelly deal with ill-conditioned
inter-image configurations provoked by the omnidirectional motion. The
feature tracking system developed efficiently manages the feature scarcity
produced by noise and outputs quality feature tracks, which allow robust
3D mapping of a given scene even if - due to noise - their length is shorter
than what it is usually assumed for performing stable 3D reconstructions.
The distributed reconstruction from multiple instances of SfM is attained
by applying loop-closing techniques. Our multiple reconstruction system
merges individual 3D structures and resolves the global scale problem with
minimal overlaps, whereas in the literature 3D mapping is obtained by overlapping
stretches of sequences. The performance of this system is demonstrated
in the 2-session case.
The management of noise, the stability against ill-configurations and the
robustness of our SfM system is validated on a number of experiments and
compared with state-of-the-art approaches. Possible future research areas
are also discussed
Representations for Cognitive Vision : a Review of Appearance-Based, Spatio-Temporal, and Graph-Based Approaches
The emerging discipline of cognitive vision requires a proper representation of visual information including spatial and temporal relationships, scenes, events, semantics and context. This review article summarizes existing representational schemes in computer vision which might be useful for cognitive vision, a and discusses promising future research directions. The various approaches are categorized according to appearance-based, spatio-temporal, and graph-based representations for cognitive vision. While the representation of objects has been covered extensively in computer vision research, both from a reconstruction as well as from a recognition point of view, cognitive vision will also require new ideas how to represent scenes. We introduce new concepts for scene representations and discuss how these might be efficiently implemented in future cognitive vision systems
Iterative Superquadric Recomposition of 3D Objects from Multiple Views
Humans are good at recomposing novel objects, i.e. they can identify
commonalities between unknown objects from general structure to finer detail,
an ability difficult to replicate by machines. We propose a framework, ISCO, to
recompose an object using 3D superquadrics as semantic parts directly from 2D
views without training a model that uses 3D supervision. To achieve this, we
optimize the superquadric parameters that compose a specific instance of the
object, comparing its rendered 3D view and 2D image silhouette. Our ISCO
framework iteratively adds new superquadrics wherever the reconstruction error
is high, abstracting first coarse regions and then finer details of the target
object. With this simple coarse-to-fine inductive bias, ISCO provides
consistent superquadrics for related object parts, despite not having any
semantic supervision. Since ISCO does not train any neural network, it is also
inherently robust to out-of-distribution objects. Experiments show that,
compared to recent single instance superquadrics reconstruction approaches,
ISCO provides consistently more accurate 3D reconstructions, even from images
in the wild. Code available at https://github.com/ExplainableML/ISCO .Comment: Accepted at ICCV 202
Problems and related results in Algebraic Vision and Multiview Geometry
The article presents a survey of results in algebraic vision and multiview
geometry. The starting points is the study of projective algebraic varieties
critical for scene reconstruction. Initially studied for reconstructing static
scenes in three-dimensional spaces, these critical loci are later investigated
for dynamic and segmented scenes in higher-dimensional projective spaces. The
formal analysis of the ideals of critical loci employs Grassmann tensors,
introduced as crucial tools for determining these ideals and aiding the
reconstruction process away from critical loci. A long-term goal of the authors
with other co-authors involves two main aspects: firstly studying properties of
Grassmann tensors, as rank, multi-rank and core, along with the varieties that
parameterize these tensors; concurrently conducting an analysis of families of
critical loci in various scenarios
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