Real-time Neural Rendering of Large-scale Scenes on the Web

Kernkonzepte

City-on-Web, the first method for real-time rendering of large-scale scenes on the web, proposes a block-based volume rendering approach and a Level-of-Detail strategy to overcome the computational, memory, and bandwidth limitations on commodity devices.

Zusammenfassung

The paper presents City-on-Web, a method for real-time neural rendering of large-scale scenes on the web. The key contributions are:

Block-based Volume Rendering:

The scene is partitioned into multiple blocks, and each block is rendered using a separate shader.
A block-based volume rendering strategy is introduced to ensure 3D consistency and correct occlusion between blocks.
This approach maintains the original structure and flow of the content during the training and rendering stages.

Level-of-Detail (LOD) Strategy:

Multiple LODs are generated for the scene to reduce resource usage and enable efficient rendering from elevated viewpoints.
The LODs are generated by downsampling the virtual grid and retraining a shared deferred MLP across merged blocks.
This dynamic loading and unloading of resources significantly reduces the memory demands during rendering.

Optimization and Baking:

The training process involves various losses, including Charbonnier loss, S3IM loss, and regularization terms for opacity and deferred MLPs.
The baking stage renders all training rays and retains samples with high opacity and weight values, which are then used to mark occupied voxels and bake high-resolution 2D planes and a low-resolution 3D voxel grid for each block.

The experiments demonstrate that City-on-Web achieves real-time rendering of large-scale scenes at approximately 32FPS with an RTX 3060 GPU, while maintaining rendering quality comparable to the current state-of-the-art methods.

Statistiken

The paper does not provide any specific numerical data or metrics in the main text. The focus is on the technical details of the proposed method.

Zitate

There are no direct quotes from the content that are particularly striking or support the key logics.

Wichtige Erkenntnisse aus

City-on-Web

by Kaiwen Song,... um arxiv.org 04-02-2024

https://arxiv.org/pdf/2312.16457.pdf

Tiefere Fragen

How can the block-based volume rendering strategy be extended to handle dynamic scenes or scenes with moving objects

To extend the block-based volume rendering strategy to handle dynamic scenes or scenes with moving objects, several adjustments and considerations need to be made. One approach could involve implementing a dynamic block allocation system that can adapt to changes in the scene. This system would need to track the movement of objects within the scene and adjust the allocation of blocks accordingly. Additionally, incorporating techniques such as predictive rendering or motion estimation could help anticipate the movement of objects and optimize block rendering in real-time. By dynamically updating the allocation of blocks based on the scene's dynamics, the system can ensure accurate rendering of moving objects while maintaining real-time performance.

What are the potential limitations or trade-offs of the LOD generation approach, and how could it be further improved to maintain visual quality at lower LODs

The LOD generation approach in the City-on-Web framework may have potential limitations and trade-offs that need to be addressed for further improvement. One limitation could be the loss of fine details and visual quality at lower LODs due to downsampling and retraining. To mitigate this, techniques such as adaptive LOD selection based on scene complexity or incorporating multi-resolution neural networks could be explored. Additionally, refining the LOD generation process by optimizing the retraining of shared deferred MLPs and enhancing the regularization of deferred MLPs could help maintain visual quality across different LODs. By fine-tuning the LOD generation process, the framework can achieve better balance between resource efficiency and visual fidelity.

Could the City-on-Web framework be adapted to work with other neural rendering techniques beyond NeRF, such as voxel-based or mesh-based representations, to further enhance its versatility and performance

The City-on-Web framework can be adapted to work with other neural rendering techniques beyond NeRF to enhance its versatility and performance. For voxel-based representations, the block-based volume rendering strategy can be modified to accommodate voxel grids and adapt the rendering process accordingly. Mesh-based representations can also be integrated by developing specialized rendering pipelines that support mesh data structures and incorporate mesh refinement techniques. By extending the framework to support a variety of neural rendering techniques, City-on-Web can cater to a broader range of scene reconstruction requirements and offer enhanced rendering capabilities for diverse applications.