HaLo-NeRF: Learning Geometry-Guided Semantics for Exploring Unconstrained Photo Collections (2404.16845v2)

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-LLMs 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-LLMs 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 LLMs. 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 paper introduces HaLo-NeRF, which augments neural radiance fields with semantic insights by distilling pseudo-labels from web data.
• It employs a hybrid method that adapts vision-language models for pixel-level semantic localization, outperforming 2D segmentation baselines.
• Its strong performance on the HolyScenes benchmark demonstrates enhanced architectural understanding for virtual landmark exploration.

HaLo-NeRF: Geometry-Guided Semantics in Large-Scale Photo Collections

The paper "HaLo-NeRF: Learning Geometry-Guided Semantics for Exploring Unconstrained Photo Collections" endeavors to augment neural radiance fields (NeRF) with a semantic understanding to enable more intuitive exploration of large-scale landmarks captured in-the-wild. This approach is particularly pertinent in scenarios where traditional geometric reconstruction methodologies have fallen short in providing a comprehensive semantic interface.

The authors articulate that internet-derived photo collections of major tourist sites lack semantic depth when limited to geometric visualization. This gap is partially attributed to the limitations of current vision-and-LLMs, which, despite excelling in broad semantic understanding, struggle with the architectural lexicon requisite for accurately comprehending these sites. Consequently, the authors propose a method that enriches the semantic understanding of such models, leveraging web-scale data and weakly-related text to imbue them with domain-specific knowledge.

The proposed framework, HaLo-NeRF, encapsulates a novel localization system that associates the neural depiction of landmarks with text-based semantic descriptions. This involves a multi-step adaptation and localization process. Initially, an LLM distills pseudo-labels from accompanying text metadata of image collections, hence harnessing its broad linguistic knowledge to mitigate the noise characteristic of textual annotations on online platforms. The authors advance this by semantically adapting pretrained vision-LLMs through web-scraped data, which employs an innovative fine-tuning strategy to bolster pixel-level semantic understanding.

From a methodological standpoint, the paper delineates a hybrid approach: instead of learning a generalized set of features for open-world queries, it optimizes specific volumetric probabilities per concept, facilitating precise 3D localization. This strategic advantage manifests in the model's ability to outperform both 2D segmentation baselines and state-of-the-art 3D language field methods, demonstrating superior capability in understanding and navigating complex architectural scenes.

Quantitatively, HaLo-NeRF exhibits prominent performance on the newly introduced HolyScenes benchmark, which comprises scenes annotated with semantic labels like portal, window, and tower. The benchmark itself is an important contribution, offering a rigorous dataset for evaluating the performance of semantic localization techniques in real-world large-scale scenes. HaLo-NeRF's integration of geometry-guided semantics reveals its strength in scenarios where semantic categories are closely tied to architectural features, significantly surpassing the performance metrics of existing techniques.

The implications of this research are manifold. Practically, HaLo-NeRF allows users to explore landmarks virtually, empowering users with an enriched comprehension of intricate architectural sites without physical travel. Theoretically, this work demonstrates how semantic knowledge can be effectively distilled and integrated into 3D neural representations, suggesting potential routes for improving language-informed AI systems. In the foreseeable future, the methodologies employed here could be leveraged across broader AI contexts, enhancing systems where spatial and semantic comprehension intersect.

Overall, this paper informs ongoing developments in AI research, specifically within the lens of geometry-driven semantics and its applications in understanding complex, unconstrained scenes. Such advancements not only hold promise for tourism and virtual exploration sectors but also contribute to the scholarly discourse on fusing vision and LLMs with 3D scene understanding.