HexPlane: A Fast Representation for Dynamic Scenes (2301.09632v2)

Abstract: Modeling and re-rendering dynamic 3D scenes is a challenging task in 3D vision. Prior approaches build on NeRF and rely on implicit representations. This is slow since it requires many MLP evaluations, constraining real-world applications. We show that dynamic 3D scenes can be explicitly represented by six planes of learned features, leading to an elegant solution we call HexPlane. A HexPlane computes features for points in spacetime by fusing vectors extracted from each plane, which is highly efficient. Pairing a HexPlane with a tiny MLP to regress output colors and training via volume rendering gives impressive results for novel view synthesis on dynamic scenes, matching the image quality of prior work but reducing training time by more than $100\times$. Extensive ablations confirm our HexPlane design and show that it is robust to different feature fusion mechanisms, coordinate systems, and decoding mechanisms. HexPlane is a simple and effective solution for representing 4D volumes, and we hope they can broadly contribute to modeling spacetime for dynamic 3D scenes.

• The paper introduces an explicit six-plane representation that reduces training complexity by over 100x compared to traditional NeRF approaches.
• It decomposes spatiotemporal data into fused 2D planar features, enabling efficient rendering and robust novel view synthesis.
• The approach sets a new benchmark for dynamic scene modeling with applications in AR/VR and real-time 3D reconstruction.

HexPlane: A Fast Representation for Dynamic Scenes

In the field of 3D computer vision, the efficient modeling and rendering of dynamic scenes remains a formidable challenge. The paper "HexPlane: A Fast Representation for Dynamic Scenes" introduces an innovative framework—HexPlane—for representing dynamic 3D scenes. Unlike traditional approaches relying heavily on implicit neural representations, which demand extensive computation due to repeated evaluations of Multi-layer Perceptrons (MLPs), HexPlane offers a more streamlined and computationally efficient method.

Core Contributions

HexPlane conceptualizes a dynamic 3D scene by leveraging explicit representations through six planes of learned features, referred to as HexPlanes. This construct allows for the synthesis of novel views by mapping spacetime points to feature vectors which are processed with a small MLP to regress color outputs. This process notably reduces training complexity and time, achieving a training time reduction of over 100 times when benchmarked against existing NeRF-based systems, while maintaining competitive image quality.

Several key features of HexPlane make this feasible:

• Dimensionality Reduction Through Planar Representations: The HexPlane structure effectively decomposes the 4D spatiotemporal volume into six 2D planes. This structure addresses the computational bottlenecks by scaling memory use with the square of the spatial resolution rather than the fourth power, as required by a naive dense 4D grid.
• Feature Extraction and Fusion: Each spatial-temporal point queried in HexPlane undergoes feature extraction from three planes that are combined and processed for rendering. The fusion of features from these planes is critical to achieving a robust representation.
• Efficiency and Scalability: By pairing HexPlanes with a minimalistic MLP, the authors have demonstrated scalability and substantial improvements in processing time. The approach effectively decouples spatial and temporal information processing, with basis sharing across timeframes to accommodate sparse temporal observations.

Implications and Future Directions

The HexPlane model not only improves computational efficiency but also broadens the applicability of dynamic scene modeling. Its explicit representation paradigm facilitates integration into a variety of real-world applications, ranging from augmented and virtual reality environments to dynamic scene reconstruction. The authors envisage the potential utility of HexPlane for tasks like dynamic object detection or 4D reconstruction.

Looking forward, there are several avenues for extending this work:

• Integration with Other Network Architectures: Combining HexPlane with architectures that utilize displacement fields or more complex deformation modeling could enhance realism in specific scenarios, such as those with frequent topology changes.
• Optimization and Generalization: Further exploration of the feature fusion mechanisms and basis functions used in HexPlane could yield insights into optimizing quality and performance balance. Additionally, applications could extend beyond vision to fields involving temporal data, such as action recognition or behavioral analysis in 4D spaces.
• Addressing Limitations in Sparse Data: Additional research into robustly managing very sparse data, possibly through hybridization with generative models or enhanced feature learning, could increase HexPlane's fidelity across challenging datasets or real-world data constraints.

In conclusion, HexPlane stands as a substantial advancement in the modeling of dynamic 3D environments. Its emphasis on explicit representation and computational efficiency establishes a new baseline for future research directed toward real-time applications and richly detailed scene reconstruction. HexPlane offers a promising stride forward in addressing current limitations in dynamic scene synthesis and stands to influence a wide spectrum of AI-driven technologies.