Related papers: Object Modeling from Underwater Forward-Scan Sonar Imagery with Sea-Surface Multipath

Object Modeling from Underwater Forward-Scan Sonar Imagery with Sea-Surface Multipath

URL: http://arxiv.org/abs/2409.06815v1
Date: Tue, 10 Sep 2024 18:46:25 GMT
Title: Object Modeling from Underwater Forward-Scan Sonar Imagery with Sea-Surface Multipath
Authors: Yuhan Liu, Shahriar Negaharipour,
Abstract summary: A key contribution, for objects imaged in the proximity of the sea surface, is to resolve the multipath artifacts due to the air-water interface. Here, the object image formed by the direct target backscatter is almost always corrupted by the ghost and sometimes by the mirror components. We model, localize, and discard the corrupted object region within each view, thus avoiding the distortion of recovered 3-D shape.
Score: 16.057203527513632
License: http://creativecommons.org/licenses/by/4.0/
Abstract: We propose an optimization technique for 3-D underwater object modeling from 2-D forward-scan sonar images at known poses. A key contribution, for objects imaged in the proximity of the sea surface, is to resolve the multipath artifacts due to the air-water interface. Here, the object image formed by the direct target backscatter is almost always corrupted by the ghost and sometimes by the mirror components (generated by the multipath propagation). Assuming a planar air-water interface, we model, localize, and discard the corrupted object region within each view, thus avoiding the distortion of recovered 3-D shape. Additionally, complementary visual cues from the boundary of the mirror component, distinct at suitable sonar poses, are employed to enhance the 3-D modeling accuracy. The optimization is implemented as iterative shape adjustment by displacing the vertices of triangular patches in the 3-D surface mesh model, in order to minimize the discrepancy between the data and synthesized views of the 3-D object model. To this end, we first determine 2-D motion fields that align the object regions in the data and synthesized views, then calculate the 3-D motion of triangular patch centers, and finally the model vertices. The 3-D model is initialized with the solution of an earlier space carving method applied to the same data. The same parameters are applied in various experiments with 2 real data sets, mixed real-synthetic data set, and computer-generated data guided by general findings from a real experiment, to explore the impact of non-flat air-water interface. The results confirm the generation of a refined 3-D model in about half-dozen iterations.

Related papers

Enhancing Single Image to 3D Generation using Gaussian Splatting and Hybrid Diffusion Priors [17.544733016978928]
3D object generation from a single image involves estimating the full 3D geometry and texture of unseen views from an unposed RGB image captured in the wild. Recent advancements in 3D object generation have introduced techniques that reconstruct an object's 3D shape and texture. We propose bridging the gap between 2D and 3D diffusion models to address this limitation.
arXiv Detail & Related papers (2024-10-12T10:14:11Z)
VFMM3D: Releasing the Potential of Image by Vision Foundation Model for Monocular 3D Object Detection [80.62052650370416]
monocular 3D object detection holds significant importance across various applications, including autonomous driving and robotics. In this paper, we present VFMM3D, an innovative framework that leverages the capabilities of Vision Foundation Models (VFMs) to accurately transform single-view images into LiDAR point cloud representations.
arXiv Detail & Related papers (2024-04-15T03:12:12Z)
ComboVerse: Compositional 3D Assets Creation Using Spatially-Aware Diffusion Guidance [76.7746870349809]
We present ComboVerse, a 3D generation framework that produces high-quality 3D assets with complex compositions by learning to combine multiple models. Our proposed framework emphasizes spatial alignment of objects, compared with standard score distillation sampling.
arXiv Detail & Related papers (2024-03-19T03:39:43Z)
MoDA: Modeling Deformable 3D Objects from Casual Videos [84.29654142118018]
We propose neural dual quaternion blend skinning (NeuDBS) to achieve 3D point deformation without skin-collapsing artifacts. In the endeavor to register 2D pixels across different frames, we establish a correspondence between canonical feature embeddings that encodes 3D points within the canonical space. Our approach can reconstruct 3D models for humans and animals with better qualitative and quantitative performance than state-of-the-art methods.
arXiv Detail & Related papers (2023-04-17T13:49:04Z)
3D Surface Reconstruction in the Wild by Deforming Shape Priors from Synthetic Data [24.97027425606138]
Reconstructing the underlying 3D surface of an object from a single image is a challenging problem. We present a new method for joint category-specific 3D reconstruction and object pose estimation from a single image. Our approach achieves state-of-the-art reconstruction performance across several real-world datasets.
arXiv Detail & Related papers (2023-02-24T20:37:27Z)
AutoShape: Real-Time Shape-Aware Monocular 3D Object Detection [15.244852122106634]
We propose an approach for incorporating the shape-aware 2D/3D constraints into the 3D detection framework. Specifically, we employ the deep neural network to learn distinguished 2D keypoints in the 2D image domain. For generating the ground truth of 2D/3D keypoints, an automatic model-fitting approach has been proposed.
arXiv Detail & Related papers (2021-08-25T08:50:06Z)
Learning Geometry-Guided Depth via Projective Modeling for Monocular 3D Object Detection [70.71934539556916]
We learn geometry-guided depth estimation with projective modeling to advance monocular 3D object detection. Specifically, a principled geometry formula with projective modeling of 2D and 3D depth predictions in the monocular 3D object detection network is devised. Our method remarkably improves the detection performance of the state-of-the-art monocular-based method without extra data by 2.80% on the moderate test setting.
arXiv Detail & Related papers (2021-07-29T12:30:39Z)
Aug3D-RPN: Improving Monocular 3D Object Detection by Synthetic Images with Virtual Depth [64.29043589521308]
We propose a rendering module to augment the training data by synthesizing images with virtual-depths. The rendering module takes as input the RGB image and its corresponding sparse depth image, outputs a variety of photo-realistic synthetic images. Besides, we introduce an auxiliary module to improve the detection model by jointly optimizing it through a depth estimation task.
arXiv Detail & Related papers (2021-07-28T11:00:47Z)
An Effective Loss Function for Generating 3D Models from Single 2D Image without Rendering [0.0]
Differentiable rendering is a very successful technique that applies to a Single-View 3D Reconstruction. Currents use losses based on pixels between a rendered image of some 3D reconstructed object and ground-truth images from given matched viewpoints to optimise parameters of the 3D shape. We propose a novel effective loss function that evaluates how well the projections of reconstructed 3D point clouds cover the ground truth object's silhouette.
arXiv Detail & Related papers (2021-03-05T00:02:18Z)
Cylinder3D: An Effective 3D Framework for Driving-scene LiDAR Semantic Segmentation [87.54570024320354]
State-of-the-art methods for large-scale driving-scene LiDAR semantic segmentation often project and process the point clouds in the 2D space. A straightforward solution to tackle the issue of 3D-to-2D projection is to keep the 3D representation and process the points in the 3D space. We develop a 3D cylinder partition and a 3D cylinder convolution based framework, termed as Cylinder3D, which exploits the 3D topology relations and structures of driving-scene point clouds.
arXiv Detail & Related papers (2020-08-04T13:56:19Z)

This list is automatically generated from the titles and abstracts of the papers in this site.