INPC: Implicit Neural Point Clouds for Radiance Field Rendering (2403.16862v2)

Abstract: We introduce a new approach for reconstruction and novel view synthesis of unbounded real-world scenes. In contrast to previous methods using either volumetric fields, grid-based models, or discrete point cloud proxies, we propose a hybrid scene representation, which implicitly encodes the geometry in a continuous octree-based probability field and view-dependent appearance in a multi-resolution hash grid. This allows for extraction of arbitrary explicit point clouds, which can be rendered using rasterization. In doing so, we combine the benefits of both worlds and retain favorable behavior during optimization: Our novel implicit point cloud representation and differentiable bilinear rasterizer enable fast rendering while preserving the fine geometric detail captured by volumetric neural fields. Furthermore, this representation does not depend on priors like structure-from-motion point clouds. Our method achieves state-of-the-art image quality on common benchmarks. Furthermore, we achieve fast inference at interactive frame rates, and can convert our trained model into a large, explicit point cloud to further enhance performance.

• The paper introduces a hybrid scene representation by combining an octree-based point probability field with a multi-resolution hash grid for appearance modeling.
• The paper utilizes viewpoint-specific sampling and differentiable bilinear splatting to efficiently optimize both geometric and appearance components.
• The paper demonstrates state-of-the-art image quality with improved structural similarity and peak signal-to-noise ratios, advancing real-time rendering applications.

Implicit Neural Point Clouds for Efficient and Detailed Radiance Field Rendering

Introduction

The pursuit of interactive novel view synthesis from sparsely sampled real-world scenes has seen significant advancements with variety in approach, from volumetric radiance fields to point-based methodologies. The recent work on Implicit Neural Point Clouds (INPC) melds the strengths of volumetric and point-based renderings, establishing a novel scene representations that capitalizes on the best of both paradigms. Central to this advancement is an octree-based point probability field in unison with a multi-resolution hash grid for appearance modeling. This synergistic approach not only promises state-of-the-art image fidelity but also paves the path to real-time rendering efficiencies.

Scene Representation

The core innovation of INPC lies in its unique scene representation that bifurcates geometric and appearance information into discrete components. By utilizing an octree to encode a point probability field, INPC achieves a scalable and efficient representation of scene geometry. Concurrently, the utilization of a multi-resolution hash grid for the storage of appearance information enables the model to maintain high levels of detail without overburdening computational resources. Together, these two components form the foundation of INPC, allowing for detailed scene reconstruction and rendering.

Methodology

INPC introduces several key methodologies to optimize and render its novel scene representation:

• Implicit Point Cloud: The representation ingeniously incorporates both the probabilistic determination of point locations via an octree and the encoding of appearance information in a hash grid. This hybrid model facilitates robust scene reconstruction while enabling interactive frame rates during rendering.
• Viewpoint-Specific Sampling: The mechanism provides adaptive sampling strategies that tailor point cloud generation to specific viewpoints, optimizing rendering quality and performance.
• Differentiable Bilinear Splatting: This approach to rendering the sampled points enables gradients to flow back through both geometric and appearance representations, ensuring coherent optimization of the entire scene representation.

Evaluation and Results

Through comprehensive experiments on common benchmarks, INPC demonstrates exemplary performance in generating high-fidelity images, substantially outstripping traditional point-based approaches and achieving similar or higher quality than state-of-the-art volumetric methods. Particularly in detailed scene features, INPC exhibits superior capability in preserving sharpness and reducing artifacts. Quantitatively, INPC shows significant improvements in structural similarity indices and peak signal-to-noise ratios across diverse datasets.

Implications and Future Directions

The intersection of favorable optimization characteristics of volumetric methods and the computational efficiency of point-based approaches signals a significant step forward in novel-view synthesis. This hybrid methodology not only elevates the visual quality of synthesized views but also enhances the practical applicability of such technologies in interactive applications. Looking ahead, further optimizations to the octree-based data structure, alongside advancements in sampling strategies, may unlock even greater efficiencies, bridging the gap to realtime applications without sacrificing image quality.

Conclusion

Implicit Neural Point Cloud elegantly combines the advantages of volumetric and point-based rendering techniques, setting a new benchmark for image quality and rendering speed in novel-view synthesis. This novel representation and rendering method holds the promise for further advancements in interactive virtual reality experiences, immersive telepresence, and other applications demanding high-quality real-time 3D scene rendering.