Streaming Radiance Fields for 3D Video Synthesis (2210.14831v1)

Abstract: We present an explicit-grid based method for efficiently reconstructing streaming radiance fields for novel view synthesis of real world dynamic scenes. Instead of training a single model that combines all the frames, we formulate the dynamic modeling problem with an incremental learning paradigm in which per-frame model difference is trained to complement the adaption of a base model on the current frame. By exploiting the simple yet effective tuning strategy with narrow bands, the proposed method realizes a feasible framework for handling video sequences on-the-fly with high training efficiency. The storage overhead induced by using explicit grid representations can be significantly reduced through the use of model difference based compression. We also introduce an efficient strategy to further accelerate model optimization for each frame. Experiments on challenging video sequences demonstrate that our approach is capable of achieving a training speed of 15 seconds per-frame with competitive rendering quality, which attains $1000 \times$ speedup over the state-of-the-art implicit methods. Code is available at https://github.com/AlgoHunt/StreamRF.

• The paper introduces an incremental learning paradigm that efficiently models dynamic 3D scenes by tuning per-frame differences from a base model.
• It employs explicit grid representations and narrow band tuning to drastically reduce computational overhead and storage requirements.
• Empirical evaluations on N3DV and Meet Room datasets demonstrate a three orders of magnitude speedup in training time while preserving rendering quality.

Overview of "Streaming Radiance Fields for 3D Video Synthesis"

The paper "Streaming Radiance Fields for 3D Video Synthesis" introduces a novel methodology to efficiently reconstruct and synthesize novel views of dynamic 3D scenes from multi-view video data. This research addresses the computational inefficiencies of current Neural Radiance Fields (NeRF) methodologies when applied to dynamic scenes. The authors propose a highly efficient approach that leverages explicit grid representations and an incremental learning paradigm to drastically reduce computational overhead and storage requirements.

Technical Contributions

1. Incremental Learning Paradigm: The authors develop an incremental learning framework for modeling dynamic 3D scenes. Unlike traditional NeRF-based methods that train a singular, expansive model across all video frames, this approach iteratively tunes per-frame differences from a base model trained only on the initial frame. This paradigm not only reduces computational load but also rapidly adapts to changes between frames.
2. Efficient Grid Representation: Utilizing explicit grid representations similar to those in Plenoxel, the method replaces costly neural network pass-throughs with operations on voxel grids. This representation inherently alleviates the storage inefficiency typically associated with explicit grid modeling of dynamic scenes.
3. Narrow Band Tuning: To capitalize on the incremental nature of video data, the proposed method employs a narrow band tuning strategy. This restricts model updates primarily to regions with changes between consecutive frames, significantly reducing unnecessary computational operations and fostering efficient convergence.
4. Difference-Based Compression: By focusing on the grid-level differences between frames, the storage overhead is minimized. The approach employs a compression scheme that retains only the voxel differences with significant changes, achieving an average storage requirement of just 5.7 MB per frame, a marked improvement over naive sparse grid representations.
5. Pilot Model Guidance: The authors incorporate a curriculum learning inspired framework where a downsampled pilot model is trained first. The insights gained are then utilized to guide the refinement of the full-scale model, ensuring efficient and stable optimization.

Empirical Evaluation

The proposed method is empirically validated on the N3DV dataset and a custom Meet Room dataset. The experiments demonstrate a profound increase in training efficiency, achieving per-frame tuning times as fast as 15 seconds. Furthermore, the approach achieves a remarkable three orders of magnitude speedup over the state-of-the-art dynamic NeRF methods in training while maintaining competitive rendering quality. Additionally, the storage efficiency greatly surpasses existing methods reliant on explicit grids, such as Plenoxel.

Implications and Future Work

Practically, this work enables real-time processing capabilities necessary for applications like VR/AR, interactive entertainment, and real-time virtual production. Theoretically, it challenges the conventional wisdom in dynamic scene modeling by showing that explicit representations can, under the right conditions, provide scalable solutions without compromising quality.

Future work could explore hybrid models that combine the benefits of implicit neural representation with the efficiency of explicit methods. Additional avenues include extending this framework to handle more complex scenes and dynamic changes, integrating domain-specific priors to enhance robustness, and optimizing further for real-time applications.

Concluding Remarks

"Streaming Radiance Fields for 3D Video Synthesis" offers a significant contribution to the field of dynamic scene reconstructive modeling, challenging the performance limitations of traditional NeRF-based methodologies with a streamlined, efficient alternative. This work opens exciting possibilities for further developments in streaming and interactive real-time applications in computer vision and graphics domains.