Spherical formulation of geometric motion segmentation constraints in
fisheye cameras
- URL: http://arxiv.org/abs/2104.12404v1
- Date: Mon, 26 Apr 2021 08:48:12 GMT
- Title: Spherical formulation of geometric motion segmentation constraints in
fisheye cameras
- Authors: Letizia Mariotti and Ciaran Eising
- Abstract summary: We introduce a visual motion segmentation method employing spherical geometry for fisheye cameras and automoated driving.
Results are presented and analyzed that demonstrate that the proposal is an effective motion segmentation approach for direct employment on fisheye imagery.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: We introduce a visual motion segmentation method employing spherical geometry
for fisheye cameras and automoated driving. Three commonly used geometric
constraints in pin-hole imagery (the positive height, positive depth and
epipolar constraints) are reformulated to spherical coordinates, making them
invariant to specific camera configurations as long as the camera calibration
is known. A fourth constraint, known as the anti-parallel constraint, is added
to resolve motion-parallax ambiguity, to support the detection of moving
objects undergoing parallel or near-parallel motion with respect to the host
vehicle. A final constraint constraint is described, known as the spherical
three-view constraint, is described though not employed in our proposed
algorithm. Results are presented and analyzed that demonstrate that the
proposal is an effective motion segmentation approach for direct employment on
fisheye imagery.
Related papers
- Camera Calibration using a Collimator System [5.138012450471437]
This paper introduces a novel camera calibration method using a collimator system.
Based on the optical geometry of the collimator system, we prove that the relative motion between the target and camera conforms to the spherical motion model.
A closed-form solver for multiple views and a minimal solver for two views are proposed for camera calibration.
arXiv Detail & Related papers (2024-09-30T07:40:41Z) - ParticleSfM: Exploiting Dense Point Trajectories for Localizing Moving
Cameras in the Wild [57.37891682117178]
We present a robust dense indirect structure-from-motion method for videos that is based on dense correspondence from pairwise optical flow.
A novel neural network architecture is proposed for processing irregular point trajectory data.
Experiments on MPI Sintel dataset show that our system produces significantly more accurate camera trajectories.
arXiv Detail & Related papers (2022-07-19T09:19:45Z) - PolarFormer: Multi-camera 3D Object Detection with Polar Transformers [93.49713023975727]
3D object detection in autonomous driving aims to reason "what" and "where" the objects of interest present in a 3D world.
Existing methods often adopt the canonical Cartesian coordinate system with perpendicular axis.
We propose a new Polar Transformer (PolarFormer) for more accurate 3D object detection in the bird's-eye-view (BEV) taking as input only multi-camera 2D images.
arXiv Detail & Related papers (2022-06-30T16:32:48Z) - Attentive and Contrastive Learning for Joint Depth and Motion Field
Estimation [76.58256020932312]
Estimating the motion of the camera together with the 3D structure of the scene from a monocular vision system is a complex task.
We present a self-supervised learning framework for 3D object motion field estimation from monocular videos.
arXiv Detail & Related papers (2021-10-13T16:45:01Z) - Visual Odometry with an Event Camera Using Continuous Ray Warping and
Volumetric Contrast Maximization [31.627936023222052]
We present a new solution to tracking and mapping with an event camera.
The motion of the camera contains both rotation and translation, and the displacements happen in an arbitrarily structured environment.
We introduce a new solution to this problem by performing contrast in 3D.
The practical validity of our approach is supported by an application to AGV motion estimation and 3D reconstruction with a single vehicle-mounted event camera.
arXiv Detail & Related papers (2021-07-07T04:32:57Z) - Calibrated and Partially Calibrated Semi-Generalized Homographies [65.29477277713205]
We propose the first minimal solutions for estimating the semi-generalized homography given a perspective and a generalized camera.
The proposed solvers are stable and efficient as demonstrated by a number of synthetic and real-world experiments.
arXiv Detail & Related papers (2021-03-11T08:56:24Z) - Nothing But Geometric Constraints: A Model-Free Method for Articulated
Object Pose Estimation [89.82169646672872]
We propose an unsupervised vision-based system to estimate the joint configurations of the robot arm from a sequence of RGB or RGB-D images without knowing the model a priori.
We combine a classical geometric formulation with deep learning and extend the use of epipolar multi-rigid-body constraints to solve this task.
arXiv Detail & Related papers (2020-11-30T20:46:48Z) - Spatiotemporal Bundle Adjustment for Dynamic 3D Human Reconstruction in
the Wild [49.672487902268706]
We present a framework that jointly estimates camera temporal alignment and 3D point triangulation.
We reconstruct 3D motion trajectories of human bodies in events captured by multiple unsynchronized and unsynchronized video cameras.
arXiv Detail & Related papers (2020-07-24T23:50:46Z) - 3D Scene Geometry-Aware Constraint for Camera Localization with Deep
Learning [11.599633757222406]
Recently end-to-end approaches based on convolutional neural network have been much studied to achieve or even exceed 3D-geometry based traditional methods.
In this work, we propose a compact network for absolute camera pose regression.
Inspired from those traditional methods, a 3D scene geometry-aware constraint is also introduced by exploiting all available information including motion, depth and image contents.
arXiv Detail & Related papers (2020-05-13T04:15:14Z) - Spherical formulation of moving object geometric constraints for
monocular fisheye cameras [0.0]
We introduce a moving object detection algorithm for fisheye cameras used in autonomous driving.
We reformulate the three commonly used constraints in rectilinear images to spherical coordinates.
arXiv Detail & Related papers (2020-03-06T14:59:38Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.