1,016 research outputs found
Accurate and linear time pose estimation from points and lines
The final publication is available at link.springer.comThe Perspective-n-Point (PnP) problem seeks to estimate the pose of a calibrated camera from n 3Dto-2D point correspondences. There are situations, though, where PnP solutions are prone to fail because feature point correspondences cannot be reliably estimated (e.g. scenes with repetitive patterns or with low texture). In such
scenarios, one can still exploit alternative geometric entities, such as lines, yielding the so-called Perspective-n-Line (PnL) algorithms. Unfortunately, existing PnL solutions are not as accurate and efficient as their point-based
counterparts. In this paper we propose a novel approach to introduce 3D-to-2D line correspondences into a PnP formulation, allowing to simultaneously process points and lines. For this purpose we introduce an algebraic line error
that can be formulated as linear constraints on the line endpoints, even when these are not directly observable. These constraints can then be naturally integrated within the linear formulations of two state-of-the-art point-based algorithms,
the OPnP and the EPnP, allowing them to indistinctly handle points, lines, or a combination of them. Exhaustive experiments show that the proposed formulation brings remarkable boost in performance compared to only point or
only line based solutions, with a negligible computational overhead compared to the original OPnP and EPnP.Peer ReviewedPostprint (author's final draft
MLPnP - A Real-Time Maximum Likelihood Solution to the Perspective-n-Point Problem
In this paper, a statistically optimal solution to the Perspective-n-Point
(PnP) problem is presented. Many solutions to the PnP problem are geometrically
optimal, but do not consider the uncertainties of the observations. In
addition, it would be desirable to have an internal estimation of the accuracy
of the estimated rotation and translation parameters of the camera pose. Thus,
we propose a novel maximum likelihood solution to the PnP problem, that
incorporates image observation uncertainties and remains real-time capable at
the same time. Further, the presented method is general, as is works with 3D
direction vectors instead of 2D image points and is thus able to cope with
arbitrary central camera models. This is achieved by projecting (and thus
reducing) the covariance matrices of the observations to the corresponding
vector tangent space.Comment: Submitted to the ISPRS congress (2016) in Prague. Oral Presentation.
Published in ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., III-3,
131-13
Large Scale SfM with the Distributed Camera Model
We introduce the distributed camera model, a novel model for
Structure-from-Motion (SfM). This model describes image observations in terms
of light rays with ray origins and directions rather than pixels. As such, the
proposed model is capable of describing a single camera or multiple cameras
simultaneously as the collection of all light rays observed. We show how the
distributed camera model is a generalization of the standard camera model and
describe a general formulation and solution to the absolute camera pose problem
that works for standard or distributed cameras. The proposed method computes a
solution that is up to 8 times more efficient and robust to rotation
singularities in comparison with gDLS. Finally, this method is used in an novel
large-scale incremental SfM pipeline where distributed cameras are accurately
and robustly merged together. This pipeline is a direct generalization of
traditional incremental SfM; however, instead of incrementally adding one
camera at a time to grow the reconstruction the reconstruction is grown by
adding a distributed camera. Our pipeline produces highly accurate
reconstructions efficiently by avoiding the need for many bundle adjustment
iterations and is capable of computing a 3D model of Rome from over 15,000
images in just 22 minutes.Comment: Published at 2016 3DV Conferenc
GP2C: Geometric Projection Parameter Consensus for Joint 3D Pose and Focal Length Estimation in the Wild
We present a joint 3D pose and focal length estimation approach for object
categories in the wild. In contrast to previous methods that predict 3D poses
independently of the focal length or assume a constant focal length, we
explicitly estimate and integrate the focal length into the 3D pose estimation.
For this purpose, we combine deep learning techniques and geometric algorithms
in a two-stage approach: First, we estimate an initial focal length and
establish 2D-3D correspondences from a single RGB image using a deep network.
Second, we recover 3D poses and refine the focal length by minimizing the
reprojection error of the predicted correspondences. In this way, we exploit
the geometric prior given by the focal length for 3D pose estimation. This
results in two advantages: First, we achieve significantly improved 3D
translation and 3D pose accuracy compared to existing methods. Second, our
approach finds a geometric consensus between the individual projection
parameters, which is required for precise 2D-3D alignment. We evaluate our
proposed approach on three challenging real-world datasets (Pix3D, Comp, and
Stanford) with different object categories and significantly outperform the
state-of-the-art by up to 20% absolute in multiple different metrics.Comment: Accepted to International Conference on Computer Vision (ICCV) 201
Very fast solution to the PnP problem with algebraic outlier rejection
Presentado al CVPR 2014 celebrado en Columbus, Ohio (US) del 23 al 28 de junio.We propose a real-time, robust to outliers and accurate solution to the Perspective-n-Point (PnP) problem. The main advantages of our solution are twofold: first, it integrates the outlier rejection within the pose estimation pipeline with a negligible computational overhead; and second, its scalability to arbitrarily large number of correspondences. Given a set of 3D-to-2D matches, we formulate pose estimation problem as a low-rank homogeneous system where the solution lies on its 1D null space. Outlier correspondences are those rows of the linear system which perturb the null space and are progressively detected by projecting them on an iteratively estimated solution of the null space. Since our outlier removal process is based on an algebraic criterion which does not require computing the full-pose and reprojecting back all 3D points on the image plane at each step, we achieve speed gains of more than 100× compared to RANSAC strategies. An extensive experimental evaluation will show that our solution yields accurate results in situations with up to 50% of outliers, and can process more than 1000 correspondences in less than 5ms.This work has been partially funded by Spanish government under projects DPI2011-27510, IPT-2012-0630-020000, IPT-2011-1015-430000 and CICYT grant TIN2012-39203; by the EU project ARCAS FP7-ICT-2011-28761; and by the ERA-Net Chistera project ViSen PCIN-2013-047Peer Reviewe
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