fVDB: A Deep-Learning Framework for Sparse, Large-Scale, and High-Performance Spatial Intelligence (2407.01781v1)

Abstract: We present fVDB, a novel GPU-optimized framework for deep learning on large-scale 3D data. fVDB provides a complete set of differentiable primitives to build deep learning architectures for common tasks in 3D learning such as convolution, pooling, attention, ray-tracing, meshing, etc. fVDB simultaneously provides a much larger feature set (primitives and operators) than established frameworks with no loss in efficiency: our operators match or exceed the performance of other frameworks with narrower scope. Furthermore, fVDB can process datasets with much larger footprint and spatial resolution than prior works, while providing a competitive memory footprint on small inputs. To achieve this combination of versatility and performance, fVDB relies on a single novel VDB index grid acceleration structure paired with several key innovations including GPU accelerated sparse grid construction, convolution using tensorcores, fast ray tracing kernels using a Hierarchical Digital Differential Analyzer algorithm (HDDA), and jagged tensors. Our framework is fully integrated with PyTorch enabling interoperability with existing pipelines, and we demonstrate its effectiveness on a number of representative tasks such as large-scale point-cloud segmentation, high resolution 3D generative modeling, unbounded scale Neural Radiance Fields, and large-scale point cloud reconstruction.

• The paper presents a novel VDB IndexGrid structure that efficiently maps sparse 3D tensors to save memory without sacrificing performance.
• It employs GPU-accelerated grid construction and advanced HDDA ray marching to achieve 1.5x to 3x faster processing speeds compared to dense grids.
• The framework integrates optimized sparse convolution operators with PyTorch, enabling scalable large-scale 3D modeling and segmentation.

Overview of $f$VDB: A Deep-Learning Framework for 3D Spatial Intelligence

The paper presents $f$VDB, a GPU-optimized framework designed for deep learning applications on large-scale 3D data. This framework is centered around the novel VDB IndexGrid data structure, which adeptly handles sparse 3D tensors by providing a rich set of differentiable operators. The primary contributions include GPU-accelerated grid construction, advanced ray marching algorithms, and efficient sparse convolution operations, all integrated seamlessly with PyTorch—offering a broad feature set without sacrificing performance.

Core Contributions

1. VDB IndexGrid Structure: The IndexGrid is derived from the well-known NanoVDB and provides an efficient way of mapping 3D coordinates to linear indices. This allows for significant memory savings by decoupling the topology and data payload, enabling the indexing of only sparse (active) voxel values.
2. Efficient GPU Construction: The authors introduce a highly parallel algorithm for GPU-based IndexGrid construction, which supports dynamic topology changes essential for many machine learning tasks. Their approach offers competitive runtime while providing substantial memory efficiency improvements over existing methods.
3. HDDA Ray Marching: The hierarchical digital differential analyzer (HDDA) accelerates ray-marching through VDB structures by leveraging the tree hierarchy to efficiently skip empty space. This algorithm achieves 1.5x to 3x faster runtime compared to a typical dense grid ray tracing, with a significantly reduced memory footprint.
4. Sparse Convolution Operators: $f$VDB introduces several optimization strategies for sparse convolution, adapting to different scenarios based on sparsity patterns and feature depth. Notably, their approach leverages local densification techniques and optimized tensor core computations to achieve high throughput.

Numerical Results and Implications

The framework benchmarks demonstrate remarkable performance in both run-time speed and memory usage across a variety of deep-learning tasks. Compared to established frameworks like Minkowski Engine and TorchSparse++, $f$VDB sustains its performance while accommodating much larger datasets. The experimental results suggest that $f$VDB is well-suited for tasks requiring extensive spatial intelligence, such as point cloud segmentation and neural rendering.

Practical and Theoretical Implications

Practically, $f$VDB is shown to be a versatile tool for researchers and practitioners working with 3D data, as evidenced by its application to diverse tasks, including large-scale surface reconstruction and generative modeling. Theoretically, the integration of the VDB data structure within a deep-learning context prompts further exploration into how such spatial structures can benefit machine learning, particularly in optimizing memory and computational resources.

Future Developments

The authors outline prospective enhancements to $f$VDB, such as expanding the library of differentiable operators and improving convolution performance by dynamically selecting optimized kernels. These developments will likely propel $f$VDB to become a critical tool in the advancement of AI applications that require handling large, sparse 3D datasets efficiently.

In summary, $f$VDB stands as a robust framework for deep learning on 3D data, offering a comprehensive solution to the challenges posed by spatial sparsity and large-scale data processing. Its contributions to both performance and feature capabilities present significant implications for future research and development in 3D deep learning.