Evaluation of a Canonical Image Representation for Sidescan Sonar

• URL: http://arxiv.org/abs/2304.09243v1
• Date: Tue, 18 Apr 2023 19:08:12 GMT
• Title: Evaluation of a Canonical Image Representation for Sidescan Sonar
• Authors: Weiqi Xu and Li Ling and Yiping Xie and Jun Zhang and John Folkesson
• Abstract summary: Sidescan sonar (SSS) detects a wide range and provides photo-realistic images in high resolution. SSS projects the 3D seafloor to 2D images, which are distorted by the AUV's altitude, target's range and sensor's resolution. In this paper, a canonical transformation method consisting of intensity correction and slant range correction is proposed to decrease the above distortion.
• Score: 4.961559590556073
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Acoustic sensors play an important role in autonomous underwater vehicles (AUVs). Sidescan sonar (SSS) detects a wide range and provides photo-realistic images in high resolution. However, SSS projects the 3D seafloor to 2D images, which are distorted by the AUV's altitude, target's range and sensor's resolution. As a result, the same physical area can show significant visual differences in SSS images from different survey lines, causing difficulties in tasks such as pixel correspondence and template matching. In this paper, a canonical transformation method consisting of intensity correction and slant range correction is proposed to decrease the above distortion. The intensity correction includes beam pattern correction and incident angle correction using three different Lambertian laws (cos, cos2, cot), whereas the slant range correction removes the nadir zone and projects the position of SSS elements into equally horizontally spaced, view-point independent bins. The proposed method is evaluated on real data collected by a HUGIN AUV, with manually-annotated pixel correspondence as ground truth reference. Experimental results on patch pairs compare similarity measures and keypoint descriptor matching. The results show that the canonical transformation can improve the patch similarity, as well as SIFT descriptor matching accuracy in different images where the same physical area was ensonified.

Related papers

• CVT-xRF: Contrastive In-Voxel Transformer for 3D Consistent Radiance Fields from Sparse Inputs [65.80187860906115]
  We propose a novel approach to improve NeRF's performance with sparse inputs. We first adopt a voxel-based ray sampling strategy to ensure that the sampled rays intersect with a certain voxel in 3D space. We then randomly sample additional points within the voxel and apply a Transformer to infer the properties of other points on each ray, which are then incorporated into the volume rendering.
  arXiv  Detail & Related papers  (2024-03-25T15:56:17Z)
• Explicit Correspondence Matching for Generalizable Neural Radiance Fields [49.49773108695526]
  We present a new NeRF method that is able to generalize to new unseen scenarios and perform novel view synthesis with as few as two source views. The explicit correspondence matching is quantified with the cosine similarity between image features sampled at the 2D projections of a 3D point on different views. Our method achieves state-of-the-art results on different evaluation settings, with the experiments showing a strong correlation between our learned cosine feature similarity and volume density.
  arXiv  Detail & Related papers  (2023-04-24T17:46:01Z)
• A Large Scale Homography Benchmark [52.55694707744518]
  We present a large-scale dataset of Planes in 3D, Pi3D, of roughly 1000 planes observed in 10 000 images from the 1DSfM dataset. We also present HEB, a large-scale homography estimation benchmark leveraging Pi3D.
  arXiv  Detail & Related papers  (2023-02-20T14:18:09Z)
• On Robust Cross-View Consistency in Self-Supervised Monocular Depth Estimation [56.97699793236174]
  We study two kinds of robust cross-view consistency in this paper. We exploit the temporal coherence in both depth feature space and 3D voxel space for self-supervised monocular depth estimation. Experimental results on several outdoor benchmarks show that our method outperforms current state-of-the-art techniques.
  arXiv  Detail & Related papers  (2022-09-19T03:46:13Z)
• 2D LiDAR and Camera Fusion Using Motion Cues for Indoor Layout Estimation [2.6905021039717987]
  A ground robot explores an indoor space with a single floor and vertical walls, and collects a sequence of intensity images and 2D LiDAR datasets. The alignment of sensor outputs and image segmentation are computed jointly by aligning LiDAR points. The ambiguity in images for ground-wall boundary extraction is removed with the assistance of LiDAR observations.
  arXiv  Detail & Related papers  (2022-04-24T06:26:02Z)
• Feature matching for multi-epoch historical aerial images [0.0]
  We present a fully automatic approach to detecting feature correspondences between historical images taken at different times. Compared to the state-of-the-art, our method improves the image georeferencing accuracy by a factor of 2.
  arXiv  Detail & Related papers  (2021-12-08T12:28:24Z)
• Object Disparity [0.0]
  This paper proposes a different approach for solving a 3D object distance detection by detecting object disparity directly without going through a dense pixel disparity. An example squeezenet Object Disparity-SSD was constructed to demonstrate an efficient object disparity detection with comparable accuracy compared with Kitti dataset pixel disparity ground truth.
  arXiv  Detail & Related papers  (2021-08-18T02:11:28Z)
• Uncertainty-Aware Camera Pose Estimation from Points and Lines [101.03675842534415]
  Perspective-n-Point-and-Line (Pn$PL) aims at fast, accurate and robust camera localizations with respect to a 3D model from 2D-3D feature coordinates.
  arXiv  Detail & Related papers  (2021-07-08T15:19:36Z)
• Deep Contrastive Patch-Based Subspace Learning for Camera Image Signal Processing [5.678834480723395]
  We present a specific patch-based, local subspace deep neural network that improves Camera ISP to be robust to heterogeneous artifacts. We call our three-fold deep-trained model the Patch Subspace Learning Autoencoder (PSL-AE) PSL-AE encodes patches extracted from noisy a nd clean image pairs, with different artifact types or distortion levels, by contrastive learning.
  arXiv  Detail & Related papers  (2021-04-01T04:40:22Z)
• Leveraging Spatial and Photometric Context for Calibrated Non-Lambertian Photometric Stereo [61.6260594326246]
  We introduce an efficient fully-convolutional architecture that can leverage both spatial and photometric context simultaneously. Using separable 4D convolutions and 2D heat-maps reduces the size and makes more efficient.
  arXiv  Detail & Related papers  (2021-03-22T18:06:58Z)
• Revisiting Stereo Depth Estimation From a Sequence-to-Sequence Perspective with Transformers [11.669086751865091]
  Stereo depth estimation relies on optimal correspondence matching between pixels on epipolar lines in the left and right images to infer depth. In this work, we revisit the problem from a sequence-to-sequence correspondence perspective to replace cost volume construction with dense pixel matching using position information and attention. We report promising results on both synthetic and real-world datasets and demonstrate that STTR generalizes across different domains, even without fine-tuning.
  arXiv  Detail & Related papers  (2020-11-05T15:35:46Z)

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.