Image-based underwater 3D reconstruction for Cultural Heritage: from image collection to 3D. Critical steps and considerations

• URL: http://arxiv.org/abs/2010.00928v1
• Date: Fri, 2 Oct 2020 11:32:33 GMT
• Title: Image-based underwater 3D reconstruction for Cultural Heritage: from image collection to 3D. Critical steps and considerations
• Abstract summary: Underwater Cultural Heritage (CH) sites are widely spread; from ruins in coastlines up to shipwrecks in deep. The documentation and preservation of this heritage is an obligation of the mankind, dictated by the international treaties like the Convention on the Protection of the Underwater Cultural Her-itage which fosters the use of "non-destructive techniques and survey meth-ods in preference over the recovery of objects"
• Score: 2.1407984396023583
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Underwater Cultural Heritage (CH) sites are widely spread; from ruins in coastlines up to shipwrecks in deep. The documentation and preservation of this heritage is an obligation of the mankind, dictated also by the international treaties like the Convention on the Protection of the Underwater Cultural Her-itage which fosters the use of "non-destructive techniques and survey meth-ods in preference over the recovery of objects". However, submerged CH lacks in protection and monitoring in regards to the land CH and nowadays recording and documenting, for digital preservation as well as dissemination through VR to wide public, is of most importance. At the same time, it is most difficult to document it, due to inherent restrictions posed by the environ-ment. In order to create high detailed textured 3D models, optical sensors and photogrammetric techniques seems to be the best solution. This chapter dis-cusses critical aspects of all phases of image based underwater 3D reconstruc-tion process, from data acquisition and data preparation using colour restora-tion and colour enhancement algorithms to Structure from Motion (SfM) and Multi-View Stereo (MVS) techniques to produce an accurate, precise and complete 3D model for a number of applications.

Related papers

• 3D-USE: From Image-Level to Scene-Level Underwater Enhancement [54.11922584483346]
  Underwater Scene-level Enhancement (USE)<n>Uses a reliable scene representation for enhancement and a consistent enhancement target without paired enhanced 3D data.<n> Experiments on real underwater scenes show improved visibility and cross-view consistency while preserving reconstruction quality.
  arXiv  Detail & Related papers  (2026-08-28T07:34:13Z)
• Wat3R: Underwater 3D Geometry Learning without Annotations [58.91163632674025]
  Estimating 3D geometry in underwater environments presents unique challenges due to light attenuation, scattering, and the absence of large-scale, high-quality 3D annotations.<n>We propose Wat3R, a cross-domain semi-supervised learning framework designed to adapt feed-forward 3D reconstruction models from air to underwater scenes.
  arXiv  Detail & Related papers  (2026-07-09T17:59:58Z)
• Restore3D: Breathing Life into Broken Objects with Shape and Texture Restoration [41.057923153081035]
  Restoring incomplete or damaged 3D objects is crucial for cultural heritage preservation, occluded object reconstruction, and artistic design.<n>We introduce Restore3D, a novel framework that simultaneously restores both the shape and texture of broken objects using multi-view images.<n> Experiments on synthetic and real broken-object benchmarks show that Restore3D improves multi-view restoration quality and textured-mesh reconstruction over representative inpainting, completion, and reconstruction baselines.
  arXiv  Detail & Related papers  (2026-07-01T07:08:23Z)
• I3DM: Implicit 3D-aware Memory Retrieval and Injection for Consistent Video Scene Generation [56.33710337846449]
  I3DM is a novel implicit 3D-aware memory mechanism for consistent video scene generation.<n>At the core of our approach is a 3D-aware memory retrieval strategy.<n>To fully utilize the retrieved historical frames, we introduce a 3D-aligned memory injection module.
  arXiv  Detail & Related papers  (2026-03-24T16:45:40Z)
• InpaintHuman: Reconstructing Occluded Humans with Multi-Scale UV Mapping and Identity-Preserving Diffusion Inpainting [64.42884719282323]
  InpaintHuman is a novel method for generating high-fidelity, complete, and animatable avatars from occluded monocular videos.<n>Our approach employs direct pixel-level supervision to ensure identity fidelity.
  arXiv  Detail & Related papers  (2026-01-05T13:26:02Z)
• RealX3D: A Physically-Degraded 3D Benchmark for Multi-view Visual Restoration and Reconstruction [51.07270086169647]
  RealX3D groups corruptions into four families, including illumination, scattering, and blurring, and captures each at multiple severity levels.<n>Each scene includes high-resolution capture, RAW images, and dense laser scans, from which we derive world-scale meshes and metric depth.
  arXiv  Detail & Related papers  (2025-12-29T12:57:19Z)
• Oitijjo-3D: Generative AI Framework for Rapid 3D Heritage Reconstruction from Street View Imagery [1.4658400971135652]
  Oitijjo-3D is a cost-free generative AI framework that democratizes 3D cultural preservation.<n>Oitijjo-3D reconstructs faithful 3D models of heritage structures through a two-stage pipeline.<n>System produces photorealistic, metrically coherent reconstructions in seconds.
  arXiv  Detail & Related papers  (2025-11-01T02:09:26Z)
• Visual enhancement and 3D representation for underwater scenes: a review [13.809193345785388]
  Underwater visual enhancement (UVE) and underwater 3D reconstruction pose significant challenges in computer vision and AI-based tasks.<n>To advance research in these areas, we present an in-depth review from multiple perspectives.
  arXiv  Detail & Related papers  (2025-05-03T17:20:24Z)
• AquaGS: Fast Underwater Scene Reconstruction with SfM-Free Gaussian Splatting [4.0317256978754505]
  We introduce AquaGS, an SfM-free underwater scene reconstruction model based on the SeaThru algorithm.<n>Our model can complete high-precision reconstruction in 30 seconds with only 3 image inputs.
  arXiv  Detail & Related papers  (2025-05-03T12:05:57Z)
• UStyle: Waterbody Style Transfer of Underwater Scenes by Depth-Guided Feature Synthesis [3.5436187733613087]
  This paper introduces UStyle, the first data-driven learning framework for transferring waterbody styles across underwater images. We propose a novel depth-aware whitening and coloring transform (DA-WCT) mechanism that integrates physics-based waterbody synthesis. We also introduce the UF7D dataset, a curated collection of high-resolution underwater images spanning seven distinct waterbody styles.
  arXiv  Detail & Related papers  (2025-03-14T21:49:40Z)
• PE3R: Perception-Efficient 3D Reconstruction [54.730257992806116]
  Perception-Efficient 3D Reconstruction (PE3R) is a novel framework designed to enhance both accuracy and efficiency. The framework achieves a minimum 9-fold speedup in 3D semantic field reconstruction, along with substantial gains in perception accuracy and reconstruction precision.
  arXiv  Detail & Related papers  (2025-03-10T16:29:10Z)
• Cutting-edge 3D reconstruction solutions for underwater coral reef images: A review and comparison [19.711398917623438]
  Photogrammetry-based approaches stand out among existing solutions. There remains a lack of systematic reviews of cutting-edge solutions specifically applied to underwater coral reef images. This paper focuses on the two critical stages of these approaches: camera pose estimation and dense surface reconstruction.
  arXiv  Detail & Related papers  (2025-02-27T14:53:15Z)
• Aquatic-GS: A Hybrid 3D Representation for Underwater Scenes [6.549998173302729]
  We propose Aquatic-GS, a hybrid 3D representation approach for underwater scenes that effectively represents both the objects and the water medium. Specifically, we construct a Neural Water Field (NWF) to implicitly model the water parameters, while extending the latest 3D Gaussian Splatting (3DGS) to model the objects explicitly. Both components are integrated through a physics-based underwater image formation model to represent complex underwater scenes.
  arXiv  Detail & Related papers  (2024-10-31T22:24:56Z)
• UW-SDF: Exploiting Hybrid Geometric Priors for Neural SDF Reconstruction from Underwater Multi-view Monocular Images [63.32490897641344]
  We propose a framework for reconstructing target objects from multi-view underwater images based on neural SDF. We introduce hybrid geometric priors to optimize the reconstruction process, markedly enhancing the quality and efficiency of neural SDF reconstruction.
  arXiv  Detail & Related papers  (2024-10-10T16:33:56Z)
• Automatic occlusion removal from 3D maps for maritime situational awareness [1.7661845949769064]
  Traditional 3D reconstruction techniques often face problems with dynamic objects, like cars or vessels, that obscure the true environment. Our approach leverages deep learning techniques, including instance segmentation and generative inpainting, to directly modify both the texture and geometry of 3D meshes. By selectively targeting occluding objects and preserving static elements, the method enhances both geometric and visual accuracy.
  arXiv  Detail & Related papers  (2024-09-05T11:58:36Z)
• Zero-Shot Multi-Object Scene Completion [59.325611678171974]
  We present a 3D scene completion method that recovers the complete geometry of multiple unseen objects in complex scenes from a single RGB-D image. Our method outperforms the current state-of-the-art on both synthetic and real-world datasets.
  arXiv  Detail & Related papers  (2024-03-21T17:59:59Z)
• Coloring the Past: Neural Historical Buildings Reconstruction from Archival Photography [69.93897305312574]
  We introduce an approach to reconstruct the geometry of historical buildings, employing volumetric rendering techniques. We leverage dense point clouds as a geometric prior and introduce a color appearance embedding loss to recover the color of the building given limited available color images.
  arXiv  Detail & Related papers  (2023-11-29T16:59:45Z)
• Shakes on a Plane: Unsupervised Depth Estimation from Unstabilized Photography [54.36608424943729]
  We show that in a ''long-burst'', forty-two 12-megapixel RAW frames captured in a two-second sequence, there is enough parallax information from natural hand tremor alone to recover high-quality scene depth. We devise a test-time optimization approach that fits a neural RGB-D representation to long-burst data and simultaneously estimates scene depth and camera motion.
  arXiv  Detail & Related papers  (2022-12-22T18:54:34Z)
• Seafloor-Invariant Caustics Removal from Underwater Imagery [0.0]
  Caustics are complex physical phenomena resulting from the projection of light rays being refracted by the wavy surface. In this work, we propose a novel method for correcting the effects of caustics on shallow underwater imagery. In particular, the developed method employs deep learning architectures in order to classify image pixels to "non-caustics" and "caustics"
  arXiv  Detail & Related papers  (2022-12-20T11:11:02Z)
• Towards Hard-pose Virtual Try-on via 3D-aware Global Correspondence Learning [70.75369367311897]
  3D-aware global correspondences are reliable flows that jointly encode global semantic correlations, local deformations, and geometric priors of 3D human bodies. An adversarial generator takes the garment warped by the 3D-aware flow, and the image of the target person as inputs, to synthesize the photo-realistic try-on result.
  arXiv  Detail & Related papers  (2022-11-25T12:16:21Z)
• AvatarMe: Realistically Renderable 3D Facial Reconstruction "in-the-wild" [105.28776215113352]
  AvatarMe is the first method that is able to reconstruct photorealistic 3D faces from a single "in-the-wild" image with an increasing level of detail. It outperforms the existing arts by a significant margin and reconstructs authentic, 4K by 6K-resolution 3D faces from a single low-resolution image.
  arXiv  Detail & Related papers  (2020-03-30T22:17:54Z)

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.