6 research outputs found
3D curves reconstruction from multiple images
In this paper, we propose a new approach for reconstructing 3D curves from a sequence of 2D images taken by uncalibrated cameras. A curve in 3D space is represented by a sequence of 3D points sampled along the curve, and the 3D points are reconstructed by minimizing the distances from their projections to the measured 2D curves on different images (i.e., 2D curve reprojection error). The minimization problem is solved by an iterative algorithm which is guaranteed to converge to a (local) minimum of the 2D reprojection error. Without requiring calibrated cameras or additional point features, our method can reconstruct multiple 3D curves simultaneously from multiple images and it readily handles images with missing and/or partially occluded curves. © 2010 IEEE.published_or_final_versionThe 2010 International Conference on Digital Image Computing: Techniques and Applications (DICTA), Sydney, Australia, 1-3 December 2010. In Proceedings of DICTA, 2010, p. 462-46
Vid2Curve: Simultaneous Camera Motion Estimation and Thin Structure Reconstruction from an RGB Video
Thin structures, such as wire-frame sculptures, fences, cables, power lines,
and tree branches, are common in the real world. It is extremely challenging to
acquire their 3D digital models using traditional image-based or depth-based
reconstruction methods because thin structures often lack distinct point
features and have severe self-occlusion. We propose the first approach that
simultaneously estimates camera motion and reconstructs the geometry of complex
3D thin structures in high quality from a color video captured by a handheld
camera. Specifically, we present a new curve-based approach to estimate
accurate camera poses by establishing correspondences between featureless thin
objects in the foreground in consecutive video frames, without requiring visual
texture in the background scene to lock on. Enabled by this effective
curve-based camera pose estimation strategy, we develop an iterative
optimization method with tailored measures on geometry, topology as well as
self-occlusion handling for reconstructing 3D thin structures. Extensive
validations on a variety of thin structures show that our method achieves
accurate camera pose estimation and faithful reconstruction of 3D thin
structures with complex shape and topology at a level that has not been
attained by other existing reconstruction methods.Comment: Accepted by SIGGRAPH 202
CURVEFUSION: Reconstructing Thin Structures from RGBD Sequences
We introduce CurveFusion, the first approach for high quality scanning of thin structures at interactive rates using a handheld RGBD camera. Thin filament-like structures are mathematically just 1D curves embedded in R3, and integration-based reconstruction works best when depth sequences (from the thin structure parts) are fused using the object's (unknown) curve skeleton. Thus, using the complementary but noisy color and depth channels, CurveFusion first automatically identifies point samples on potential thin structures and groups them into bundles, each being a group of a fixed number of aligned consecutive frames. Then, the algorithm extracts per-bundle skeleton curves using L1 axes, and aligns and iteratively merges the L1 segments from all the bundles to form the final complete curve skeleton. Thus, unlike previous methods, reconstruction happens via integration along a data-dependent fusion primitive, i.e., the extracted curve skeleton. We extensively evaluate CurveFusion on a range of challenging examples, different scanner and calibration settings, and present high fidelity thin structure reconstructions previously just not possible from raw RGBD sequences
Exploiting Structural Regularities and Beyond: Vision-based Localization and Mapping in Man-Made Environments
Image-based estimation of camera motion, known as visual odometry
(VO), plays a very important role in many robotic applications
such as control and navigation of unmanned mobile robots,
especially when no external navigation reference signal is
available. The core problem of VO is the estimation of the
camera’s ego-motion (i.e. tracking) either between successive
frames, namely relative pose estimation, or with respect to a
global map, namely absolute pose estimation. This thesis aims to
develop efficient, accurate and robust VO solutions by taking
advantage of structural regularities in man-made environments,
such as piece-wise planar structures, Manhattan World and more
generally, contours and edges. Furthermore, to handle challenging
scenarios that are beyond the limits of classical sensor based VO
solutions, we investigate a recently emerging sensor — the
event camera and study on event-based mapping — one of the key
problems in the event-based VO/SLAM. The main achievements are
summarized as follows.
First, we revisit an old topic on relative pose estimation:
accurately and robustly estimating the fundamental matrix given a
collection of independently estimated homograhies. Three
classical methods are reviewed and then we show a simple but
nontrivial two-step normalization
within the direct linear method that achieves similar performance
to the less attractive and more computationally intensive
hallucinated points based method.
Second, an efficient 3D rotation estimation algorithm for depth
cameras in piece-wise planar environments is presented. It shows
that by using surface normal vectors as an input, planar modes in
the corresponding density distribution function can be discovered
and continuously
tracked using efficient non-parametric estimation techniques. The
relative rotation can be estimated by registering entire bundles
of planar modes by using robust L1-norm minimization.
Third, an efficient alternative to the iterative closest point
algorithm for real-time tracking of modern depth cameras in
ManhattanWorlds is developed. We exploit the common orthogonal
structure of man-made environments in order to decouple the
estimation of the rotation and the three degrees of freedom of
the translation. The derived camera orientation is absolute and
thus free of long-term drift, which in turn benefits the accuracy
of the translation estimation as well.
Fourth, we look into a more general structural
regularity—edges. A real-time VO system that uses Canny edges
is proposed for RGB-D cameras. Two novel alternatives to
classical distance transforms are developed with great properties
that significantly improve the classical Euclidean distance field
based methods in terms of efficiency, accuracy and robustness.
Finally, to deal with challenging scenarios that go beyond what
standard RGB/RGB-D cameras can handle, we investigate the
recently emerging event camera and focus on the problem of 3D
reconstruction from data captured by a stereo event-camera rig
moving in a static
scene, such as in the context of stereo Simultaneous Localization
and Mapping
Reconstruction of general curves, using factorization and bundle adjustment
In this paper, we extend the notion of affine shape, introduced by Sparr, from finite point sets to curves. The extension makes it possible to reconstruct 3D-curves up to projective transformations, from a number of their 2D-projections. We also extend the bundle adjustment technique from point features to curves. The first step of the curve reconstruction algorithm is based on affine shape. It is independent of choice of coordinates, is robust, does not rely on any preselected parameters and works for an arbitrary number of images. In particular this means that, except for a small set of curves (e.g. a moving line), a solution is given to the aperture problem of finding point correspondences between curves. The second step takes advantage of any knowledge of measurement errors in the images. This is possible by extending the bundle adjustment technique to curves. Finally, experiments are performed on both synthetic and real data to show the performance and applicability of the algorithm