One is All: Bridging the Gap Between Neural Radiance Fields Architectures with Progressive Volume Distillation (2211.15977v3)

Abstract: Neural Radiance Fields (NeRF) methods have proved effective as compact, high-quality and versatile representations for 3D scenes, and enable downstream tasks such as editing, retrieval, navigation, etc. Various neural architectures are vying for the core structure of NeRF, including the plain Multi-Layer Perceptron (MLP), sparse tensors, low-rank tensors, hashtables and their compositions. Each of these representations has its particular set of trade-offs. For example, the hashtable-based representations admit faster training and rendering but their lack of clear geometric meaning hampers downstream tasks like spatial-relation-aware editing. In this paper, we propose Progressive Volume Distillation (PVD), a systematic distillation method that allows any-to-any conversions between different architectures, including MLP, sparse or low-rank tensors, hashtables and their compositions. PVD consequently empowers downstream applications to optimally adapt the neural representations for the task at hand in a post hoc fashion. The conversions are fast, as distillation is progressively performed on different levels of volume representations, from shallower to deeper. We also employ special treatment of density to deal with its specific numerical instability problem. Empirical evidence is presented to validate our method on the NeRF-Synthetic, LLFF and TanksAndTemples datasets. For example, with PVD, an MLP-based NeRF model can be distilled from a hashtable-based Instant-NGP model at a 10X~20X faster speed than being trained the original NeRF from scratch, while achieving a superior level of synthesis quality. Code is available at https://github.com/megvii-research/AAAI2023-PVD.

• The paper introduces Progressive Volume Distillation to enable architecture-agnostic conversion among diverse NeRF models, drastically reducing training time.
• It employs a block-wise distillation process that progressively refines volume representations while addressing density instability issues.
• Empirical results demonstrate up to 20x faster training and superior synthesis quality when converting models between disparate NeRF frameworks.

Detailed Analysis of Progressive Volume Distillation in Neural Radiance Fields Architectures

The paper, "One is All: Bridging the Gap Between Neural Radiance Fields Architectures with Progressive Volume Distillation," introduces Progressive Volume Distillation (PVD) as a novel methodology to stimulate cross-model learning and optimized conversions among disparate Neural Radiance Field (NeRF) architectures. The research addresses the varied architectural frameworks currently dominating the NeRF landscape, such as Multi-Layer Perceptrons (MLPs), sparse tensors, low-rank tensors, and hashtables. Each of these frameworks presents specific trade-offs in terms of either performance efficiency or geometric interpretability.

Motivation

The problem space this paper inhabits is characterized by the significant diversity of representations in Novel View Synthesis (NVS) applications that NeRF addresses. Practitioners in areas like scene editing, retrieval, and rendering often face the challenge of selecting the most appropriate architecture given the constraints and demands of their application. PVD is introduced as a way to enable fluid transitions between representations, thus granting greater flexibility and adaptability in the choice of NeRF frameworks tailored to specific computational and application contexts.

Methodology

The core contribution of this paper is the PVD framework which achieves architecture agnostic conversions. This is realized through a distillation process that progressively refines volume representations from less detailed to more detailed depictions. The paper details a strategic block-wise distillation that significantly expedites the training pipeline, markedly reducing computational time compared to training models from scratch. Moreover, the methodology incorporates a special processing of the density component within the volume data, addressing numerical instability issues that were previously noted in density handling.

Results and Validation

Empirical evaluations demonstrate compelling performance results using datasets such as NeRF-Synthetic, LLFF, and Tanks and Temples. A noteworthy finding is the capability of an MLP-based NeRF model, distilled from a hashtable-based Instant-NGP model, to achieve synthesis qualities superior to those trained from scratch, and achieve training speed improvements by an order of magnitude (10-20x). The conversion process retains visual fidelity across model types, evidencing the robustness of the PVD framework.

Implications and Future Work

The practical implications of this work are profound; they indicate that complex and resource-intensive aspects of NeRF training and adaptation can be substantially streamlined without loss of data representational quality. Theoretically, this approach suggests potential for more general application to other neural architecture transitions, promoting a more versatile handling of learned neural representations in variety of settings.

Moreover, the future work suggested could involve exploring more deeply the potential of this framework to compress and optimize neural models beyond the current forms discussed. Further research may also investigate pragmatic deployment in real-time environments that require immediate adjustments to neural architectures based on dynamic input data.

In summarizing, this paper sets the stage for a more adaptive and inter-operable future in the deployment of NeRF technologies, promoting a paradigm where architecture decisions can be more fluidly tailored to the specific needs of application requirements and resource availability.