Readings on SIGGRAPH 2024 Honorable Mention Papers

I have read two articles that primarily discuss advancements in 3D Gaussian Splatting (3D GS) and Neural Radiance Fields (NeRF), both of which are popular in recent years. Both of them are the honorable mentions in SIGGRAPH 2024. “Bilateral Guided Radiance Field Processing” and “SMERF: Streamable Memory Efficient Radiance Fields for Real-Time Large-Scene Exploration”. 

"Bilateral Guided Radiance Field Processing" by Wang et al. (2024)
Introduction
The paper "Bilateral Guided Radiance Field Processing" by Yuehao Wang et al. tackles the challenge of achieving consistent high-quality novel view synthesis using neural radiance fields (NeRFs). While NeRFs excel in generating photorealistic images from multi-view data, they often struggle with inconsistencies, known as "floaters," due to varying in-camera image signal processing (ISP). This research introduces a novel method to disentangle these ISP effects during training and apply user-desired enhancements post-training.
Technical Core
The core technical contribution of this paper is the development and integration of a 4D bilateral grid into the NeRF framework to address and mitigate the issue of floaters and enhance multi-view consistency. The method can be divided into two primary stages:
1.	NeRF Training with Bilateral Grids:
Disentangling ISP Effects: The authors propose optimizing 3D bilateral grids alongside the NeRF model during training. This approach approximates the effects of ISP processing in 3D space.
Construction and Optimization: Bilateral grids are constructed to capture both spatial and intensity variations. During training, these grids are optimized to model the ISP effects, ensuring that the synthesized views remain consistent and free of artifacts.
Training Process: The NeRF is trained with these optimized grids, which helps in reducing artifacts like floaters. This ensures that the scene's radiance field is accurately reconstructed from multiple views while accounting for the ISP variations.
2.	Radiance Finishing with 4D Bilateral Grids:
4D Bilateral Grid: After the initial training, a low-rank 4D bilateral grid is used to capture and reapply user-desired enhancements across the entire 3D scene. The 4D grid extends the spatial and intensity dimensions to include time or other relevant factors, providing a comprehensive enhancement model.
User-Driven Enhancements: This stage allows users to make specific enhancements, such as adjusting brightness or contrast, which are then consistently applied to all views using the 4D bilateral grid.
Application of Enhancements: The enhancements stored in the 4D bilateral grid are reintroduced to the synthesized views, ensuring that the final output maintains high visual fidelity and consistency across all views.
The bilateral grids, particularly the 4D version, provide a robust mechanism to model and reapply ISP effects, ensuring consistent and high-quality novel view synthesis.
Importance and Acceptance
This paper is crucial because it addresses a significant issue in NeRF-based novel view synthesis—the presence of floaters and other inconsistencies due to ISP effects. The novel integration of 4D bilateral grids presents a powerful solution to this problem, enhancing the visual quality and consistency of the generated images. This method is particularly important for applications requiring high visual fidelity and consistency across multiple views, such as virtual reality, 3D reconstruction, and augmented reality.
The paper was likely accepted due to its innovative approach to a well-known problem, the effectiveness of the proposed solution, and the comprehensive experimental validation. The authors demonstrated the method's superiority over existing techniques, particularly in challenging scenarios, which likely contributed to its acceptance.

"SMERF: Streamable Memory Efficient Radiance Fields" by Duckworth et al. (2024)
Introduction
The paper "SMERF: Streamable Memory Efficient Radiance Fields" by Benjamin Duckworth et al. presents a novel approach to reducing the memory footprint and improving the streaming capabilities of neural radiance fields (NeRFs). This is particularly important for real-time applications where computational resources and memory are limited, such as in mobile devices or real-time 3D streaming.
Technical Core
The technical core of this paper revolves around the development of Streamable Memory Efficient Radiance Fields (SMERF), a new representation for NeRFs that balances memory efficiency and rendering quality. The key innovations include:
1.	Hierarchical Representation:
Data Structure: SMERF employs a hierarchical data structure, such as an octree, that allows for progressive refinement of the radiance field. An octree is a tree data structure in which each internal node has exactly eight children. This structure is useful for representing 3D spaces efficiently.
Progressive Refinement: The hierarchical representation allows for the initial transmission of a coarse version of the radiance field, which can be progressively refined by adding finer details as more data is streamed. This approach is well-suited for scenarios where bandwidth or memory is limited, as it allows for adaptive level-of-detail rendering.
2.	Memory Compression:
Sparse Voxel Octrees: SMERF uses sparse voxel octrees to store the radiance field data efficiently. Voxelization involves dividing the 3D space into small, cube-shaped units called voxels. Sparse voxel octrees only store voxels that contain relevant data, significantly reducing memory usage.
Quantization: Advanced quantization techniques are employed to compress the radiance field data further. Quantization involves reducing the precision of the stored data to save memory, while still maintaining sufficient accuracy for high-quality rendering.
3.	Adaptive Streaming:
Resolution Adjustment: The framework supports adaptive streaming, where the resolution and detail level of the radiance field can be dynamically adjusted based on network conditions and device capabilities. This ensures that high-quality rendering is achieved without overwhelming the available resources.
Real-Time Adaptation: The system can adjust the level of detail in real-time, providing a smooth and responsive experience for users. This is particularly important for applications like virtual reality, where maintaining a consistent frame rate is crucial for user comfort.
The combination of these techniques ensures that SMERF can deliver high-quality novel view synthesis in a memory-efficient and streamable manner.
Importance and Acceptance
The importance of SMERF lies in its potential to make high-quality neural rendering accessible on devices with limited computational power and memory. This has broad implications for various applications, including gaming, virtual reality, and remote visualization. By addressing the challenges of memory usage and streaming, SMERF enables more widespread and practical use of NeRFs in real-time scenarios.
The paper was likely accepted due to its significant contributions to the field of neural rendering, particularly in making NeRFs more practical for real-world applications. The innovative hierarchical and adaptive approaches to memory management and streaming, coupled with rigorous experimental validation, demonstrate the effectiveness and potential impact of the proposed method.
Conclusion
Both papers make significant contributions to the field of neural radiance fields, addressing key challenges in multi-view consistency and memory efficiency. The integration of bilateral grids in Wang et al.'s work provides a robust solution for handling ISP-induced inconsistencies, while Duckworth et al.'s SMERF framework makes NeRFs more practical for real-time applications. These advancements are crucial for the continued development and deployment of high-quality 3D rendering technologies.