Tirtha -- An Automated Platform to Crowdsource Images and Create 3D
Models of Heritage Sites
- URL: http://arxiv.org/abs/2308.01246v2
- Date: Tue, 15 Aug 2023 17:39:05 GMT
- Title: Tirtha -- An Automated Platform to Crowdsource Images and Create 3D
Models of Heritage Sites
- Abstract summary: Digital preservation of Cultural Heritage (CH) sites is crucial to protect them against damage from natural disasters or human activities.
Creating 3D models of CH sites has become a popular method of digital preservation thanks to advancements in computer vision and photogrammetry.
We propose Tirtha, a web platform for crowdsourcing images of CH sites and creating their 3D models.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: Digital preservation of Cultural Heritage (CH) sites is crucial to protect
them against damage from natural disasters or human activities. Creating 3D
models of CH sites has become a popular method of digital preservation thanks
to advancements in computer vision and photogrammetry. However, the process is
time-consuming, expensive, and typically requires specialized equipment and
expertise, posing challenges in resource-limited developing countries.
Additionally, the lack of an open repository for 3D models hinders research and
public engagement with their heritage. To address these issues, we propose
Tirtha, a web platform for crowdsourcing images of CH sites and creating their
3D models. Tirtha utilizes state-of-the-art Structure from Motion (SfM) and
Multi-View Stereo (MVS) techniques. It is modular, extensible and
cost-effective, allowing for the incorporation of new techniques as
photogrammetry advances. Tirtha is accessible through a web interface at
https://tirtha.niser.ac.in and can be deployed on-premise or in a cloud
environment. In our case studies, we demonstrate the pipeline's effectiveness
by creating 3D models of temples in Odisha, India, using crowdsourced images.
These models are available for viewing, interaction, and download on the Tirtha
website. Our work aims to provide a dataset of crowdsourced images and 3D
reconstructions for research in computer vision, heritage conservation, and
related domains. Overall, Tirtha is a step towards democratizing digital
preservation, primarily in resource-limited developing countries.
Related papers
- 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) - 3D Engine-ready Photorealistic Avatars via Dynamic Textures [6.040118855896928]
We propose an end-to-end pipeline that builds explicitly-represented 3D avatars using standard 3D assets.
Our key idea is the use of dynamically-generated textures to enhance the realism and visually mask deficiencies in the underlying mesh geometry.
arXiv Detail & Related papers (2025-03-19T07:19:50Z) - Creating Seamless 3D Maps Using Radiance Fields [0.0]
It is desirable to create 3D object models and 3D maps from 2D input images for applications such as navigation, virtual tourism, and urban planning.
Traditional methods have difficulty with reflective surfaces and specular reflections.
Google Road View does not create an actual 3D object model, only a collection of views.
arXiv Detail & Related papers (2024-03-17T22:49:07Z) - A Comprehensive Survey on 3D Content Generation [148.434661725242]
3D content generation shows both academic and practical values.
New taxonomy is proposed that categorizes existing approaches into three types: 3D native generative methods, 2D prior-based 3D generative methods, and hybrid 3D generative methods.
arXiv Detail & Related papers (2024-02-02T06:20:44Z) - AG3D: Learning to Generate 3D Avatars from 2D Image Collections [96.28021214088746]
We propose a new adversarial generative model of realistic 3D people from 2D images.
Our method captures shape and deformation of the body and loose clothing by adopting a holistic 3D generator.
We experimentally find that our method outperforms previous 3D- and articulation-aware methods in terms of geometry and appearance.
arXiv Detail & Related papers (2023-05-03T17:56:24Z) - Visual Localization using Imperfect 3D Models from the Internet [54.731309449883284]
This paper studies how imperfections in 3D models affect localization accuracy.
We show that 3D models from the Internet show promise as an easy-to-obtain scene representation.
arXiv Detail & Related papers (2023-04-12T16:15:05Z) - Persistent Nature: A Generative Model of Unbounded 3D Worlds [74.51149070418002]
We present an extendable, planar scene layout grid that can be rendered from arbitrary camera poses via a 3D decoder and volume rendering.
Based on this representation, we learn a generative world model solely from single-view internet photos.
Our approach enables scene extrapolation beyond the fixed bounds of current 3D generative models, while also supporting a persistent, camera-independent world representation.
arXiv Detail & Related papers (2023-03-23T17:59:40Z) - Towers of Babel: Combining Images, Language, and 3D Geometry for
Learning Multimodal Vision [50.07532560364523]
We present a new, large-scale dataset of landmark photo collections that contains descriptive text in the form of captions and hierarchical category names.
WikiScenes forms a new testbed for multimodal reasoning involving images, text, and 3D geometry.
arXiv Detail & Related papers (2021-08-12T17:16:49Z) - Procedural 3D Terrain Generation using Generative Adversarial Networks [0.0]
We use Generative Adversarial Networks (GAN) to yield realistic 3D environments based on the distribution of remotely sensed images of landscapes, captured by satellites or drones.
We are able to construct 3D scenery consisting of a plausible height distribution and colorization, in relation to the remotely sensed landscapes provided during training.
arXiv Detail & Related papers (2020-10-13T14:15:10Z) - SMPLpix: Neural Avatars from 3D Human Models [56.85115800735619]
We bridge the gap between classic rendering and the latest generative networks operating in pixel space.
We train a network that directly converts a sparse set of 3D mesh vertices into photorealistic images.
We show the advantage over conventional differentiables both in terms of the level of photorealism and rendering efficiency.
arXiv Detail & Related papers (2020-08-16T10:22:00Z) - Attention-based 3D Object Reconstruction from a Single Image [0.2519906683279153]
We propose to substantially improve Occupancy Networks, a state-of-the-art method for 3D object reconstruction.
We apply the concept of self-attention within the network's encoder in order to leverage complementary input features.
We were able to improve the original work in 5.05% of mesh IoU, 0.83% of Normal Consistency, and more than 10X the Chamfer-L1 distance.
arXiv Detail & Related papers (2020-08-11T14:51:18Z)
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.