Efficient Representation of Light Fields for High-Quality Novel View Synthesis in Diverse Natural Scenes

Core Concepts

NeLF-Pro, a novel representation that models a scene's light field using spatially distributed light field feature probes, achieves efficient and high-quality novel view synthesis across diverse natural scenes of varying scales.

Abstract

The paper presents NeLF-Pro, a novel representation for modeling and reconstructing light fields in diverse natural scenes. Unlike previous methods that represent the 3D scene globally, NeLF-Pro models the light field as a set of local light field feature probes, parameterized with position and multi-channel 2D feature maps. The key ideas are: Decompose the scene into local light field probes to enable scale- and trajectory-agnostic modeling, rather than encoding positions globally. Introduce a Vector-Matrix-Matrix (VMM) factorization technique to compactly represent the light field feature probes as products of shared core factors and basis factors, enabling the discovery of hidden relationships and patterns within the scene. Propose a soft localization and blending algorithm to enable fast reconstruction and aliasing-free rendering by selecting and blending features from neighboring probes. The authors demonstrate that NeLF-Pro significantly boosts the performance of feature grid-based representations, achieving fast reconstruction with better rendering quality while maintaining compact modeling. Experiments on diverse scenes, including small-scale, medium-scale, and large-scale datasets, show that NeLF-Pro outperforms or matches state-of-the-art methods in terms of rendering quality and efficiency.

Stats

NeLF-Pro achieves PSNR of 27.02 on the Free dataset and 27.27 on the mip-NeRF360 dataset, outperforming feature grid-based methods. On the KITTI-360 dataset, NeLF-Pro achieves PSNR of 22.55, SSIM of 0.829, and LPIPS of 0.201, outperforming NeRF, mip-NeRF, and PNF. On large-scale scenes, NeLF-Pro achieves PSNR of 28.86 on 56-Leonard, 27.84 on Scuol, and 22.68 on KITTI-360-big, while being more efficient and compact than the baselines.

Quotes

"Our central idea is to bake the scene's light field into spatially varying learnable representations and to query point features by weighted blending of probes close to the camera - allowing for mipmap representation and rendering." "We leverage the fact that a full light field can be seen as an integration of light field samples, where each light field sample can be approximated by blending a collection of discrete light field images - which have been widely applied in Image Based Rendering (IBR)." "Together with the local spherical projections, this enables storing of a 3D point at different probes in a form of multi-scale pyramid (also known as mipmaps) - nearby areas are modeled at higher resolution, while distant areas are represented at lower resolution."

Key Insights Distilled From

NeLF-Pro: Neural Light Field Probes for Multi-Scale Novel View Synthesis

by Zinuo You,An... at arxiv.org 04-23-2024

https://arxiv.org/pdf/2312.13328.pdf

Deeper Inquiries

How can the proposed VMM factorization technique be extended to model more complex scene properties, such as time-varying or dynamic light fields

The VMM factorization technique proposed in the NeLF-Pro framework can be extended to model more complex scene properties, such as time-varying or dynamic light fields, by incorporating temporal information into the factorization process. This can be achieved by introducing an additional dimension to the factorization process to account for changes in the light field over time. One approach could be to include a time-dependent factor in the factorization model, allowing the representation to capture the evolution of the light field over different time steps. By incorporating temporal dynamics into the factorization, the model can learn to encode not only spatial variations but also temporal changes in the scene's lighting properties. This would enable the NeLF-Pro framework to handle dynamic scenes where the light field varies over time, such as in animated sequences or real-time applications. Additionally, techniques from video processing and temporal modeling could be integrated into the factorization process to capture the temporal coherence and consistency in the light field representation. By considering the spatio-temporal relationships in the scene, the VMM factorization can adapt to time-varying light fields and provide a more comprehensive representation of dynamic scenes.

What are the potential applications of the NeLF-Pro representation beyond novel view synthesis, such as in the fields of augmented reality or computational photography

The NeLF-Pro representation offers a wide range of potential applications beyond novel view synthesis, extending into various fields such as augmented reality (AR) and computational photography. Augmented Reality (AR): NeLF-Pro can be utilized in AR applications to enhance the realism and accuracy of virtual objects and scenes overlaid onto the real world. By leveraging the detailed and spatially varying light field probes, AR systems can achieve more realistic lighting and shading effects, leading to a seamless integration of virtual and real elements. Computational Photography: In computational photography, NeLF-Pro can be employed for advanced image processing tasks, such as relighting, depth estimation, and scene reconstruction. By utilizing the compact and efficient representation of light field probes, computational photography algorithms can benefit from improved scene understanding and enhanced image quality. Virtual Reality (VR): NeLF-Pro can also find applications in VR environments to create immersive and realistic virtual worlds. By accurately modeling light fields and scene properties, VR experiences can be enhanced with high-fidelity rendering and novel view synthesis capabilities, providing users with a more engaging and immersive virtual environment. Light Field Imaging: NeLF-Pro can be applied in light field imaging systems to capture and reconstruct complex light fields in real-world scenes. By utilizing the distributed light field probes, advanced imaging systems can achieve high-quality light field acquisition and rendering, enabling new possibilities in photography and cinematography.

Can the NeLF-Pro framework be adapted to handle other types of scene representations, such as point clouds or meshes, to enable a more unified approach to 3D scene modeling and reconstruction

The NeLF-Pro framework can be adapted to handle other types of scene representations, such as point clouds or meshes, to enable a more unified approach to 3D scene modeling and reconstruction. By incorporating different scene representations into the NeLF-Pro framework, it can offer a versatile and comprehensive solution for various 3D scene modeling tasks. Point Clouds: To handle point clouds, the NeLF-Pro framework can be modified to incorporate point-based features and spatial relationships. By converting point cloud data into a suitable format for the light field probes, the framework can effectively model and reconstruct scenes represented as point clouds, enabling accurate 3D scene understanding and rendering. Meshes: For mesh-based representations, the NeLF-Pro framework can be extended to encode geometric and radiance information from mesh data. By integrating mesh structures into the factorization process, the framework can capture detailed surface properties and lighting effects, facilitating realistic rendering and reconstruction of mesh-based scenes. Hybrid Representations: NeLF-Pro can also support hybrid scene representations that combine point clouds, meshes, and light field probes. By leveraging the strengths of different representation types, the framework can provide a unified approach to 3D scene modeling, accommodating diverse scene complexities and characteristics for a wide range of applications in computer vision, graphics, and beyond.