Neural Geometric Level of Detail: Real-time Rendering with Implicit 3D Shapes (2101.10994v1)

Abstract: Neural signed distance functions (SDFs) are emerging as an effective representation for 3D shapes. State-of-the-art methods typically encode the SDF with a large, fixed-size neural network to approximate complex shapes with implicit surfaces. Rendering with these large networks is, however, computationally expensive since it requires many forward passes through the network for every pixel, making these representations impractical for real-time graphics. We introduce an efficient neural representation that, for the first time, enables real-time rendering of high-fidelity neural SDFs, while achieving state-of-the-art geometry reconstruction quality. We represent implicit surfaces using an octree-based feature volume which adaptively fits shapes with multiple discrete levels of detail (LODs), and enables continuous LOD with SDF interpolation. We further develop an efficient algorithm to directly render our novel neural SDF representation in real-time by querying only the necessary LODs with sparse octree traversal. We show that our representation is 2-3 orders of magnitude more efficient in terms of rendering speed compared to previous works. Furthermore, it produces state-of-the-art reconstruction quality for complex shapes under both 3D geometric and 2D image-space metrics.

• The paper introduces an octree-based neural SDF approach that achieves continuous LOD with 2-3 orders of magnitude faster rendering.
• The methodology leverages a sparse voxel octree and a compact MLP to encode and interpolate feature vectors for high-quality geometric reconstruction.
• Experimental results demonstrate state-of-the-art performance in memory efficiency, geometric accuracy, and visual fidelity across multiple datasets.

Neural Geometric Level of Detail: Real-time Rendering with Implicit 3D Shapes

The paper "Neural Geometric Level of Detail: Real-time Rendering with Implicit 3D Shapes" explores an advanced representation for 3D shapes using neural signed distance functions (SDFs), which has shown promise in efficiently encoding complex geometries. The authors address limitations in existing methodologies that use large, fixed-size neural networks, which are computationally intensive and unsuitable for real-time graphics applications.

Key Innovations

The authors introduce a novel neural representation that leverages an octree-based feature volume. This approach is designed to adaptively fit shapes with multiple discrete levels of detail (LODs), facilitating a continuous LOD through SDF interpolation. A sparse octree traversal is used to query only necessary LODs, which significantly enhances the rendering efficiency and speeds up the process by 2-3 orders of magnitude compared to earlier methods. Their method achieves state-of-the-art reconstruction quality with both 3D geometric and 2D image-space metrics.

Methodology

The core of the proposed method lies in encoding neural SDFs into a sparse voxel octree (SVO), where feature vectors are associated with voxel corners. This design enables efficient representation and interpolation of LODs. A small multi-layer perceptron (MLP) is utilized to compute signed distances from these features. The MLP’s parameters are optimized jointly with the feature vectors to ensure that each LOD accurately reconstructs geometry.

Training involves sampling from multiple distributions (uniform, surface-based, and perturbed surface samples) to optimize the model for reconstructing complex shapes. The method is designed to efficiently render geometry through a tailored sphere-tracing algorithm which utilizes adaptive ray steps and ray-octree intersection techniques for optimal performance.

Experimental Results

The authors' experiments demonstrate the effectiveness of their approach across several datasets, including ShapeNet, Thingi10K, and TurboSquid. Their representation achieves superior performance in terms of geometric accuracy and visual fidelity, while significantly reducing computational requirements. Notably, the method outperforms other advanced neural representations, such as DeepSDF, FFN, and SIREN, in both memory efficiency and quality of rendered output.

Analyses of analytic SDFs further showcase the model's capability in capturing intricate geometric details and handling non-metric distance fields which traditional models struggle with. Moreover, the proposed representation shows rapid convergence during training, making it highly efficient for practical applications.

Implications and Future Directions

The introduction of Neural Geometric LOD presents significant implications for the field of real-time rendering. By enabling real-time interactions with complex 3D shapes, this work has potential applications in areas like virtual reality, autonomous navigation, and interactive content creation.

Future work could explore extending this approach to handle larger scenes and more complex geometries that include fine structures unrepresented by current configurations. Additionally, integrating this method with dynamic or deformable geometries could broaden its applicability in animation and simulation contexts.

In summary, this paper contributes a highly efficient method for real-time rendering of 3D shapes using neural representations, advancing the state of art in geometric modeling and visualization.