arXiv:2404.16845v1 Announce Type: new Abstract: Internet image collections containing photos captured by crowds of photographers show promise for enabling digital exploration of large-scale tourist landmarks. However, prior works focus primarily on geometric reconstruction and visualization, neglecting the key role of language in providing a semantic interface for navigation and fine-grained understanding. In constrained 3D domains, recent methods have leveraged vision-and-language models as a strong prior of 2D visual semantics. While these models display an excellent understanding of broad visual semantics, they struggle with unconstrained photo collections depicting such tourist landmarks, as they lack expert knowledge of the architectural domain. In this work, we present a localization system that connects neural representations of scenes depicting large-scale landmarks with text describing a semantic region within the scene, by harnessing the power of SOTA vision-and-language models with adaptations for understanding landmark scene semantics. To bolster such models with fine-grained knowledge, we leverage large-scale Internet data containing images of similar landmarks along with weakly-related textual information. Our approach is built upon the premise that images physically grounded in space can provide a powerful supervision signal for localizing new concepts, whose semantics may be unlocked from Internet textual metadata with large language models. We use correspondences between views of scenes to bootstrap spatial understanding of these semantics, providing guidance for 3D-compatible segmentation that ultimately lifts to a volumetric scene representation. Our results show that HaLo-NeRF can accurately localize a variety of semantic concepts related to architectural landmarks, surpassing the results of other 3D models as well as strong 2D segmentation baselines. Our project page is at https://tau-vailab.github.io/HaLo-NeRF/.
The article “Internet image collections and the role of language in exploring tourist landmarks” explores the potential of internet image collections in enabling digital exploration of large-scale tourist landmarks. While previous works have focused on geometric reconstruction and visualization, this article highlights the importance of language in providing a semantic interface for navigation and understanding. The authors present a localization system that connects neural representations of scenes with text describing specific semantic regions within the scene. By leveraging large-scale internet data and vision-and-language models, the authors aim to enhance the understanding of architectural landmarks. The results of their approach, called HaLo-NeRF, demonstrate its ability to accurately localize various semantic concepts related to architectural landmarks, surpassing other 3D models and strong 2D segmentation baselines.
An Innovative Approach to Enhancing Semantic Understanding of Large-Scale Landmarks
Exploring large-scale tourist landmarks through internet image collections has the potential to revolutionize digital navigation and understanding. However, existing research has mainly focused on geometric reconstruction and visualization, overlooking the crucial role of language in providing a semantic interface for navigation and in-depth comprehension.
In recent years, vision-and-language models have emerged as powerful tools in constrained 3D domains, offering a strong grasp of visual semantics. However, these models often struggle with unconstrained photo collections of tourist landmarks due to their lack of expert knowledge in the architectural domain.
Addressing this limitation, we present a localization system that combines neural representations of scenes depicting large-scale landmarks with text descriptions of semantic regions within the scenes. By harnessing state-of-the-art vision-and-language models, customized for understanding landmark scene semantics, our system bridges the gap between visual and linguistic information.
To enhance the knowledge base of these models, we leverage a vast amount of internet data consisting of images of similar landmarks accompanied by loosely related textual information. We believe that images physically grounded in space can serve as a powerful supervision signal for localizing new concepts. By unlocking the semantics from internet textual metadata using sophisticated language models, our system achieves a more comprehensive understanding.
To achieve this, we utilize correspondences between different views of scenes to bootstrap spatial understanding and guide 3D-compatible segmentation. This process ultimately leads to the creation of a volumetric representation of the landmark scene, providing highly accurate localization of various semantic concepts.
Through comprehensive testing, our approach, known as HaLo-NeRF, has demonstrated superior localization abilities compared to other 3D models and strong 2D segmentation baselines. By combining the power of vision-and-language models with fine-grained architectural knowledge, HaLo-NeRF opens up new possibilities for digital exploration and understanding of large-scale landmarks.
To learn more about our exciting project and explore our results, please visit our project page at https://tau-vailab.github.io/HaLo-NeRF/.
The paper titled “HaLo-NeRF: Harnessing Large-scale Internet Data for Semantic Localization of Architectural Landmarks” addresses the limitations of existing methods that focus on geometric reconstruction and visualization in internet image collections. These collections contain photos taken by crowds of photographers and offer potential for digital exploration of tourist landmarks. However, these prior works neglect the crucial role of language in providing a semantic interface for navigation and fine-grained understanding.
The authors propose a localization system that connects neural representations of scenes depicting large-scale landmarks with text describing a specific semantic region within the scene. They achieve this by leveraging state-of-the-art vision-and-language models and adapting them to understand landmark scene semantics. To enhance these models with fine-grained knowledge, they utilize large-scale internet data containing images of similar landmarks along with weakly-related textual information.
The approach is based on the idea that physically grounded images can provide a supervision signal for localizing new concepts, which can be extracted from textual metadata using large language models. By establishing correspondences between different views of scenes, the authors bootstrap spatial understanding of these semantics, providing guidance for 3D-compatible segmentation that ultimately leads to a volumetric scene representation.
The results of their experiments, presented under the name HaLo-NeRF, demonstrate the system’s ability to accurately localize various semantic concepts related to architectural landmarks. It outperforms other 3D models and strong 2D segmentation baselines in terms of accuracy.
This research is significant as it bridges the gap between visual and linguistic understanding of large-scale tourist landmarks. By incorporating both visual and textual information, the proposed system offers a more comprehensive and semantic approach to exploring and navigating these landmarks. The ability to accurately localize semantic concepts within images can greatly enhance the user experience in virtual tours, cultural preservation, and architectural research.
Moving forward, it would be interesting to see how this approach could be applied to other domains beyond architectural landmarks. Additionally, further research could explore the scalability of the system to handle even larger internet image collections and investigate the potential of incorporating other sources of textual information, such as social media posts or historical texts, to expand the system’s knowledge and understanding.
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