Fast Dynamic Radiance Fields with Time-Aware Neural Voxels (2205.15285v2)

Abstract: Neural radiance fields (NeRF) have shown great success in modeling 3D scenes and synthesizing novel-view images. However, most previous NeRF methods take much time to optimize one single scene. Explicit data structures, e.g. voxel features, show great potential to accelerate the training process. However, voxel features face two big challenges to be applied to dynamic scenes, i.e. modeling temporal information and capturing different scales of point motions. We propose a radiance field framework by representing scenes with time-aware voxel features, named as TiNeuVox. A tiny coordinate deformation network is introduced to model coarse motion trajectories and temporal information is further enhanced in the radiance network. A multi-distance interpolation method is proposed and applied on voxel features to model both small and large motions. Our framework significantly accelerates the optimization of dynamic radiance fields while maintaining high rendering quality. Empirical evaluation is performed on both synthetic and real scenes. Our TiNeuVox completes training with only 8 minutes and 8-MB storage cost while showing similar or even better rendering performance than previous dynamic NeRF methods.

• The paper introduces TiNeuVox, integrating explicit temporal encoding in neural voxels for dynamic 3D scene rendering.
• It employs multi-distance interpolation and coordinate deformation to enhance spatial and temporal feature alignment.
• TiNeuVox achieves 192x speedup with 8-minute training per scene and 8 MB memory usage, offering high-quality rendering.

Fast Dynamic Radiance Fields with Time-Aware Neural Voxels: An In-Depth Examination

The presented paper introduces a method, TiNeuVox, which extends neural radiance fields (NeRF) for efficient modeling and rendering of dynamic 3D scenes. The authors address fundamental challenges associated with optimizing dynamic NeRFs, specifically concerning temporal information and the scalability of motion representation.

Technical Contributions

The authors propose TiNeuVox, a framework integrating explicit spatial-temporal features into neural voxels. The framework significantly enhances convergence speeds while preserving rendering quality by leveraging an innovative blend of spatial data structuring and temporal encoding techniques. The paper articulates three core methodologies:

1. Time-Aware Voxels: The paper introduces a novel representation by incorporating time as a dimension to voxel features, termed as TiNeuVox. This approach effectively encodes temporal information to address the dynamic nature of scenes.
2. Multi-Distance Interpolation (MDI): TiNeuVox employs a multi-distance interpolation method, which samples voxel features across various spatial resolutions. This strategy enables comprehensive modeling of motions ranging from subtle to extensive, enhancing the fidelity of dynamic scene rendering.
3. Coordinate Deformation and Temporal Enhancement: By using a compact deformation network, TiNeuVox approximates the spatial transition of dynamic points. Additional enhancement via neural embeddings of time facilitates finer adjustments, correcting potential errors introduced by geometric approximations.

Evaluation and Results

The empirical analysis showcases TiNeuVox's substantial improvements over baseline and contemporaneous NeRF methods across a set of both synthetic and real dynamic datasets. Notably, TiNeuVox attains competitive PSNR values, with a mere 8-minute training duration per scene and 8-MB memory usage, thereby offering an acceleration of 192 times the rate of comparable frameworks like HyperNeRF.

Specifically, TiNeuVox demonstrates superior convergence efficiency and storage optimization by balancing between explicit Taylor-enhanced representation and efficient MLP-driven (multi-layer perceptron) temporal processing. Its performance in synthetic scenes illustrated not only proficiency in handling variations in motion scale but also maintaining high-quality visual outputs.

Implications and Future Perspectives

TiNeuVox contributes significantly to computational rendering for interactive media, virtual reality (VR), and augmented reality (AR) applications, where rendering speed without compromising image quality is paramount. Its scalable design further invites integration with other neural rendering advancements, potentially broadening its applicability to diversified computational photography and image synthesis scenarios.

The proposed methodologies lend themselves to probable adaptations involving further performance optimizations, such as hierarchical quality enhancement or hybrid coupling with other state-of-the-art dynamic modeling technologies. Furthermore, adaptive neural architectures leveraging TiNeuVox principles could evolve within AI-driven domains like robotics, where dynamic vision models inform real-time decision-making processes.

Practically, the research sets a precedent for evolving explicit-implicit model strategies, potentially prompting reevaluation of existing paradigms within neural representation and real-time rendering frameworks. The collaborative exploration of memory-efficient representations and time-concurrent modeling stands to redefine entrenched bottlenecks across industry-standard NeRF methodologies.

In sum, TiNeuVox presents a compelling method that integrates advanced temporal voxel features into neural radiance fields, offering substantial advantages in speed, quality, and efficiency for dynamic scene rendering. This innovative paper provides meaningful avenues for future research and application within the growing landscape of 3D imaging computation.