7 research outputs found
P1AC: Revisiting Absolute Pose From a Single Affine Correspondence
We introduce a novel solution to the problem of estimating the pose of a
calibrated camera given a single observation of an oriented point and an affine
correspondence to a reference image. Affine correspondences have traditionally
been used to improve feature matching over wide baselines; however, little
previous work has considered the use of such correspondences for absolute
camera pose computation. The advantage of our approach (P1AC) is that it
requires only a single correspondence in the minimal case in comparison to the
traditional point-based approach (P3P) which requires at least three points.
Our method removes the limiting assumptions made in previous work and provides
a general solution that is applicable to large-scale image-based localization.
Our evaluation on synthetic data shows that our approach is numerically stable
and more robust to point observation noise than P3P. We also evaluate the
application of our approach for large-scale image-based localization and
demonstrate a practical reduction in the number of iterations and computation
time required to robustly localize an image
Infrastructure-based Multi-Camera Calibration using Radial Projections
Multi-camera systems are an important sensor platform for intelligent systems
such as self-driving cars. Pattern-based calibration techniques can be used to
calibrate the intrinsics of the cameras individually. However, extrinsic
calibration of systems with little to no visual overlap between the cameras is
a challenge. Given the camera intrinsics, infrastucture-based calibration
techniques are able to estimate the extrinsics using 3D maps pre-built via SLAM
or Structure-from-Motion. In this paper, we propose to fully calibrate a
multi-camera system from scratch using an infrastructure-based approach.
Assuming that the distortion is mainly radial, we introduce a two-stage
approach. We first estimate the camera-rig extrinsics up to a single unknown
translation component per camera. Next, we solve for both the intrinsic
parameters and the missing translation components. Extensive experiments on
multiple indoor and outdoor scenes with multiple multi-camera systems show that
our calibration method achieves high accuracy and robustness. In particular,
our approach is more robust than the naive approach of first estimating
intrinsic parameters and pose per camera before refining the extrinsic
parameters of the system. The implementation is available at
https://github.com/youkely/InfrasCal.Comment: ECCV 202
Beyond Controlled Environments: 3D Camera Re-Localization in Changing Indoor Scenes
Long-term camera re-localization is an important task with numerous computer
vision and robotics applications. Whilst various outdoor benchmarks exist that
target lighting, weather and seasonal changes, far less attention has been paid
to appearance changes that occur indoors. This has led to a mismatch between
popular indoor benchmarks, which focus on static scenes, and indoor
environments that are of interest for many real-world applications. In this
paper, we adapt 3RScan - a recently introduced indoor RGB-D dataset designed
for object instance re-localization - to create RIO10, a new long-term camera
re-localization benchmark focused on indoor scenes. We propose new metrics for
evaluating camera re-localization and explore how state-of-the-art camera
re-localizers perform according to these metrics. We also examine in detail how
different types of scene change affect the performance of different methods,
based on novel ways of detecting such changes in a given RGB-D frame. Our
results clearly show that long-term indoor re-localization is an unsolved
problem. Our benchmark and tools are publicly available at
waldjohannau.github.io/RIO10Comment: ECCV 2020, project website https://waldjohannau.github.io/RIO1
Generic Primitive Detection in Point Clouds Using Novel Minimal Quadric Fits
We present a novel and effective method for detecting 3D primitives in
cluttered, unorganized point clouds, without axillary segmentation or type
specification. We consider the quadric surfaces for encapsulating the basic
building blocks of our environments - planes, spheres, ellipsoids, cones or
cylinders, in a unified fashion. Moreover, quadrics allow us to model higher
degree of freedom shapes, such as hyperboloids or paraboloids that could be
used in non-rigid settings.
We begin by contributing two novel quadric fits targeting 3D point sets that
are endowed with tangent space information. Based upon the idea of aligning the
quadric gradients with the surface normals, our first formulation is exact and
requires as low as four oriented points. The second fit approximates the first,
and reduces the computational effort. We theoretically analyze these fits with
rigor, and give algebraic and geometric arguments. Next, by re-parameterizing
the solution, we devise a new local Hough voting scheme on the null-space
coefficients that is combined with RANSAC, reducing the complexity from
to (three points). To the best of our knowledge, this is the
first method capable of performing a generic cross-type multi-object primitive
detection in difficult scenes without segmentation. Our extensive qualitative
and quantitative results show that our method is efficient and flexible, as
well as being accurate.Comment: Submitted to IEEE Transactions on Pattern Analysis and Machine
Intelligence (T-PAMI). arXiv admin note: substantial text overlap with
arXiv:1803.0719