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High-Fidelity Cross-Scale Neural Rendering of Real-World Large-Scale Scenes with Hash Featurized Manifold


Core Concepts
We propose a novel hash featurized manifold representation that fully unleashes the expressivity of volumetric hash encoding by rasterizing the surface manifold to explicitly prioritize multi-view consistency, enabling high-fidelity cross-scale neural rendering of real-world large-scale scenes.
Abstract
The authors introduce a novel scene representation called "hash featurized manifold" for high-fidelity cross-scale novel view synthesis (NVS) of real-world large-scale scenes. Key highlights: Existing representations based on explicit surface suffer from discretization resolution or UV distortion, while implicit volumetric representations lack scalability for large scenes due to dispersed weight distribution and surface ambiguity. The proposed hash featurized manifold representation leverages volumetric multi-resolution hash encoding to featurize the surface manifold, explicitly concentrating the hash entries on the 2D manifold to effectively represent highly detailed contents independent of the discretization resolution. A deferred neural rendering framework is introduced to efficiently decode the representation, with two tailored designs: surface multisampling and manifold deformation, to better express the cross-scale details. The authors introduce the GigaNVS dataset, the first real-captured dataset targeting cross-scale, high-resolution NVS of large-scale scenes, to benchmark the proposed method and state-of-the-art approaches. Extensive experiments demonstrate that the proposed method significantly outperforms prior approaches, reducing the average LPIPS by 40% on the challenging GigaNVS benchmark.
Stats
The proposed GigaNVS dataset contains 7 real-world large-scale scenes with areas ranging from 1.3×10^4 m^2 to 3×10^6 m^2, captured using a combination of aerial and ground photography, yielding a collection of 1,600 ~ 18,000 high-quality 5K/8K multi-view images per scene.
Quotes
"Existing representations based on explicit surface suffer from discretization resolution or UV distortion, while implicit volumetric representations lack scalability for large scenes due to the dispersed weight distribution and surface ambiguity." "Our key insight is to featurize the 2D surface manifold with 3D volumetric hash encoding, sidestepping the complex surface parametrization to strictly preserve geometric conformality while leveraging rasterization to concentrate the learnable hash entries on multi-view consistent signals throughout the optimization."

Key Insights Distilled From

by Guangyu Wang... at arxiv.org 03-29-2024

https://arxiv.org/pdf/2403.19517.pdf
XScale-NVS

Deeper Inquiries

How can the proposed hash featurized manifold representation be extended to handle incomplete or occluded geometry in the scene

The proposed hash featurized manifold representation can be extended to handle incomplete or occluded geometry in the scene by incorporating differentiable rendering techniques. By leveraging differentiable rendering, the representation can be optimized to better handle missing or occluded parts of the scene. This can be achieved by introducing a latent-space deformation mechanism that allows the representation to adapt to the incomplete or occluded geometry during the rendering process. Additionally, the representation can be enhanced with a surface completion module that predicts the missing parts of the geometry based on the available information, enabling more robust and accurate rendering of scenes with incomplete or occluded geometry.

What are the potential applications of the high-fidelity cross-scale neural rendering enabled by the proposed method beyond virtual reality and visual effects

The high-fidelity cross-scale neural rendering enabled by the proposed method has a wide range of potential applications beyond virtual reality and visual effects. Some of the potential applications include: Robotics Simulation: The high-fidelity rendering can be used to create realistic simulations for robotic systems, enabling more accurate training and testing of robots in virtual environments. Architectural Visualization: Architects and designers can use the detailed rendering to visualize and explore architectural designs in a realistic and immersive way, allowing for better design decisions and client presentations. Medical Imaging: The method can be applied to medical imaging tasks such as volumetric rendering of medical scans, enabling detailed and accurate visualization of anatomical structures for diagnosis and treatment planning. Autonomous Vehicles: The high-fidelity rendering can be used to generate realistic synthetic data for training autonomous vehicles, improving their perception and decision-making capabilities in complex real-world scenarios. Cultural Heritage Preservation: The method can be utilized for creating detailed digital reconstructions of historical sites and artifacts, preserving cultural heritage in a virtual environment for research and education purposes.

How can the insights from the hash featurized manifold be applied to other computer vision tasks that require efficient and expressive scene representations, such as 3D reconstruction or semantic segmentation

The insights from the hash featurized manifold representation can be applied to other computer vision tasks that require efficient and expressive scene representations, such as 3D reconstruction or semantic segmentation, in the following ways: 3D Reconstruction: The hash featurized manifold approach can be adapted for 3D reconstruction tasks by featurizing the reconstructed 3D geometry with multi-resolution hash encoding. This can enable more detailed and accurate reconstruction of complex scenes while maintaining scalability and efficiency. Semantic Segmentation: The representation can be utilized for semantic segmentation by incorporating semantic information into the hash encoding. By featurizing the scene with semantic labels in addition to geometric features, the method can provide a more comprehensive representation for semantic segmentation tasks, allowing for accurate and detailed scene understanding. Object Detection: The hash featurized manifold can be leveraged for object detection by encoding object-specific features into the representation. This can enable more precise localization and recognition of objects in complex scenes, improving the performance of object detection algorithms in challenging environments.
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