106 research outputs found
Linear Global Translation Estimation with Feature Tracks
This paper derives a novel linear position constraint for cameras seeing a
common scene point, which leads to a direct linear method for global camera
translation estimation. Unlike previous solutions, this method deals with
collinear camera motion and weak image association at the same time. The final
linear formulation does not involve the coordinates of scene points, which
makes it efficient even for large scale data. We solve the linear equation
based on norm, which makes our system more robust to outliers in
essential matrices and feature correspondences. We experiment this method on
both sequentially captured images and unordered Internet images. The
experiments demonstrate its strength in robustness, accuracy, and efficiency.Comment: Changes: 1. Adopt BMVC2015 style; 2. Combine sections 3 and 5; 3.
Move "Evaluation on synthetic data" out to supplementary file; 4. Divide
subsection "Evaluation on general data" to subsections "Experiment on
sequential data" and "Experiment on unordered Internet data"; 5. Change Fig.
1 and Fig.8; 6. Move Fig. 6 and Fig. 7 to supplementary file; 7 Change some
symbols; 8. Correct some typo
ShapeFit and ShapeKick for Robust, Scalable Structure from Motion
We introduce a new method for location recovery from pair-wise directions
that leverages an efficient convex program that comes with exact recovery
guarantees, even in the presence of adversarial outliers. When pairwise
directions represent scaled relative positions between pairs of views
(estimated for instance with epipolar geometry) our method can be used for
location recovery, that is the determination of relative pose up to a single
unknown scale. For this task, our method yields performance comparable to the
state-of-the-art with an order of magnitude speed-up. Our proposed numerical
framework is flexible in that it accommodates other approaches to location
recovery and can be used to speed up other methods. These properties are
demonstrated by extensively testing against state-of-the-art methods for
location recovery on 13 large, irregular collections of images of real scenes
in addition to simulated data with ground truth
LIFT: Learned Invariant Feature Transform
We introduce a novel Deep Network architecture that implements the full
feature point handling pipeline, that is, detection, orientation estimation,
and feature description. While previous works have successfully tackled each
one of these problems individually, we show how to learn to do all three in a
unified manner while preserving end-to-end differentiability. We then
demonstrate that our Deep pipeline outperforms state-of-the-art methods on a
number of benchmark datasets, without the need of retraining.Comment: Accepted to ECCV 2016 (spotlight
Deep Projective Rotation Estimation through Relative Supervision
Orientation estimation is the core to a variety of vision and robotics tasks
such as camera and object pose estimation. Deep learning has offered a way to
develop image-based orientation estimators; however, such estimators often
require training on a large labeled dataset, which can be time-intensive to
collect. In this work, we explore whether self-supervised learning from
unlabeled data can be used to alleviate this issue. Specifically, we assume
access to estimates of the relative orientation between neighboring poses, such
that can be obtained via a local alignment method. While self-supervised
learning has been used successfully for translational object keypoints, in this
work, we show that naively applying relative supervision to the rotational
group will often fail to converge due to the non-convexity of the
rotational space. To tackle this challenge, we propose a new algorithm for
self-supervised orientation estimation which utilizes Modified Rodrigues
Parameters to stereographically project the closed manifold of to the
open manifold of , allowing the optimization to be done in an
open Euclidean space. We empirically validate the benefits of the proposed
algorithm for rotational averaging problem in two settings: (1) direct
optimization on rotation parameters, and (2) optimization of parameters of a
convolutional neural network that predicts object orientations from images. In
both settings, we demonstrate that our proposed algorithm is able to converge
to a consistent relative orientation frame much faster than algorithms that
purely operate in the space. Additional information can be found at
https://sites.google.com/view/deep-projective-rotation/home .Comment: Conference on Robot Learning (CoRL), 2022. Supplementary material is
available at https://sites.google.com/view/deep-projective-rotation/hom
Robust Camera Location Estimation by Convex Programming
D structure recovery from a collection of D images requires the
estimation of the camera locations and orientations, i.e. the camera motion.
For large, irregular collections of images, existing methods for the location
estimation part, which can be formulated as the inverse problem of estimating
locations in
from noisy measurements of a subset of the pairwise directions
, are
sensitive to outliers in direction measurements. In this paper, we firstly
provide a complete characterization of well-posed instances of the location
estimation problem, by presenting its relation to the existing theory of
parallel rigidity. For robust estimation of camera locations, we introduce a
two-step approach, comprised of a pairwise direction estimation method robust
to outliers in point correspondences between image pairs, and a convex program
to maintain robustness to outlier directions. In the presence of partially
corrupted measurements, we empirically demonstrate that our convex formulation
can even recover the locations exactly. Lastly, we demonstrate the utility of
our formulations through experiments on Internet photo collections.Comment: 10 pages, 6 figures, 3 table
Spectral Motion Synchronization in SE(3)
This paper addresses the problem of motion synchronization (or averaging) and
describes a simple, closed-form solution based on a spectral decomposition,
which does not consider rotation and translation separately but works straight
in SE(3), the manifold of rigid motions. Besides its theoretical interest,
being the first closed form solution in SE(3), experimental results show that
it compares favourably with the state of the art both in terms of precision and
speed
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