79,322 research outputs found
SONIC: Sonar Image Correspondence using Pose Supervised Learning for Imaging Sonars
In this paper, we address the challenging problem of data association for
underwater SLAM through a novel method for sonar image correspondence using
learned features. We introduce SONIC (SONar Image Correspondence), a
pose-supervised network designed to yield robust feature correspondence capable
of withstanding viewpoint variations. The inherent complexity of the underwater
environment stems from the dynamic and frequently limited visibility
conditions, restricting vision to a few meters of often featureless expanses.
This makes camera-based systems suboptimal in most open water application
scenarios. Consequently, multibeam imaging sonars emerge as the preferred
choice for perception sensors. However, they too are not without their
limitations. While imaging sonars offer superior long-range visibility compared
to cameras, their measurements can appear different from varying viewpoints.
This inherent variability presents formidable challenges in data association,
particularly for feature-based methods. Our method demonstrates significantly
better performance in generating correspondences for sonar images which will
pave the way for more accurate loop closure constraints and sonar-based place
recognition. Code as well as simulated and real-world datasets will be made
public to facilitate further development in the field
Robust Cooperative Strategy for Contour Matching Using Epipolar Geometry
Feature matching in images plays an important role in computer vision such as for 3D reconstruction, motion analysis, object recognition, target tracking and dynamic scene analysis. In this paper, we present a robust cooperative strategy to establish the correspondence of the contours between two uncalibrated images based on the recovered epipolar geometry. We take into account two representations of contours in image as contour points and contour chains. The method proposed in the paper is composed of the following two consecutive steps: (1) The first step uses the LMedS method to estimate the fundamental matrix based on Hartley’s 8-point algorithm, (2) The second step uses a new robust cooperative strategy to match contours. The presented approach has been tested with various real images and experimental results show that our method can produce more accurate contour correspondences.Singapore-MIT Alliance (SMA
Temporally coherent 4D reconstruction of complex dynamic scenes
This paper presents an approach for reconstruction of 4D temporally coherent
models of complex dynamic scenes. No prior knowledge is required of scene
structure or camera calibration allowing reconstruction from multiple moving
cameras. Sparse-to-dense temporal correspondence is integrated with joint
multi-view segmentation and reconstruction to obtain a complete 4D
representation of static and dynamic objects. Temporal coherence is exploited
to overcome visual ambiguities resulting in improved reconstruction of complex
scenes. Robust joint segmentation and reconstruction of dynamic objects is
achieved by introducing a geodesic star convexity constraint. Comparative
evaluation is performed on a variety of unstructured indoor and outdoor dynamic
scenes with hand-held cameras and multiple people. This demonstrates
reconstruction of complete temporally coherent 4D scene models with improved
nonrigid object segmentation and shape reconstruction.Comment: To appear in The IEEE Conference on Computer Vision and Pattern
Recognition (CVPR) 2016 . Video available at:
https://www.youtube.com/watch?v=bm_P13_-Ds
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