Knowledge NeRF: Few-shot Novel View Synthesis for Dynamic Articulated Objects (2404.00674v2)

Abstract: We present Knowledge NeRF to synthesize novel views for dynamic scenes. Reconstructing dynamic 3D scenes from few sparse views and rendering them from arbitrary perspectives is a challenging problem with applications in various domains. Previous dynamic NeRF methods learn the deformation of articulated objects from monocular videos. However, qualities of their reconstructed scenes are limited. To clearly reconstruct dynamic scenes, we propose a new framework by considering two frames at a time.We pretrain a NeRF model for an articulated object.When articulated objects moves, Knowledge NeRF learns to generate novel views at the new state by incorporating past knowledge in the pretrained NeRF model with minimal observations in the present state. We propose a projection module to adapt NeRF for dynamic scenes, learning the correspondence between pretrained knowledge base and current states. Experimental results demonstrate the effectiveness of our method in reconstructing dynamic 3D scenes with 5 input images in one state. Knowledge NeRF is a new pipeline and promising solution for novel view synthesis in dynamic articulated objects. The data and implementation are publicly available at https://github.com/RussRobin/Knowledge_NeRF.

• The paper introduces a framework that reduces data requirements by synthesizing dynamic 3D views from as few as five images.
• It employs a lightweight projection module to effectively transfer pre-trained NeRF knowledge to new states for robust performance.
• Experimental results demonstrate superior fidelity with improved PSNR, SSIM, and MSE across both synthetic and real-world datasets.

Knowledge NeRF: Enhancing Few-shot Novel View Synthesis for Dynamic Articulated Objects

Introduction

The challenge of reconstructing dynamic 3D scenes from sparse views has long captivated researchers in the computer vision and related fields. The endeavor to accurately render such scenes from arbitrary new perspectives has broad applicability, from augmented reality to virtual reality and beyond. Against this backdrop, the paper on Knowledge NeRF proposes a novel framework aimed at addressing the limitations of existing Neural Radiance Fields (NeRF) methods in handling dynamic scenes, particularly those involving articulated objects.

The Core Contribution

The advent of Knowledge NeRF marks a significant step forward in the synthesis of novel views for dynamic scenes. This new framework is designed to operate by considering two frames at a time, essentially leveraging a pre-trained NeRF model of an articulated object in one state and then learning the transformation to a new state with minimal observational data. The approach is distinguished by its application of a Lightweight Projection Module, enhancing the NeRF's adaptability to dynamic scenes by learning the correspondence between the knowledge base and current states.

Key contributions of this paper include:

• Introduction of a framework that significantly reduces the requisite data for synthesizing novel views of dynamic scenes, requiring as few as 5 input images for high-quality reconstructions.
• A lightweight projection module that effectively learns the transformation between different states of an articulated object, integrating pre-existing knowledge with minimal current observational data.
• Provision of new datasets for evaluating dynamic reconstruction performance, encompassing both synthetic and real-world scenarios.

Framework Overview

At the heart of Knowledge NeRF lies the notion of leveraging existing knowledge to infer the current appearance of an articulated object. By pre-training a NeRF model in one state and then transferring knowledge to a current, altered state, the framework achieves commendable fidelity in rendering with sparse observational inputs.

The projection module plays a pivotal role in this process, mapping the correspondences between the original and current states. This module is adeptly designed to be lightweight, ensuring its straightforward incorporation and training alongside the NeRF model.

Experimental Insights

Empirical evaluations underscore the effectiveness of Knowledge NeRF. Across various datasets, the method exhibits superior performance in constructing dynamic 3D scenes from a notably limited set of input images. The framework's efficacy is particularly highlighted in comparison to other contemporary approaches, such as DS-NeRF, D-NeRF, and DietNeRF, especially when considering metrics like PSNR, SSIM, and MSE.

Furthermore, experiments reveal the framework's robustness across varied transformations, including rotations, translations, scaling, and changes in occlusion relationships. Such versatility accentuates the practical relevance of Knowledge NeRF in diverse application domains.

Future Directions

While Knowledge NeRF represents a leap forward in few-shot novel view synthesis, future research avenues beckon. The adaptability of the projection module to even more complex dynamic scenes, the integration of semantic understanding in the reconstruction process, and enhancements in computational efficiency present fertile grounds for exploration.

Moreover, the application potential of Knowledge NeRF in real-world scenarios—ranging from interactive entertainment to telepresence and beyond—warrants extensive investigation. The bridge between high-fidelity dynamic scene reconstruction and practical utility in end-user applications remains a compelling domain for further research.

Conclusion

Knowledge NeRF introduces a transformative approach to the reconstruction and rendering of dynamic 3D scenes from sparse views. By ingeniously leveraging a pre-trained NeRF model and incorporating a lightweight projection module, the framework sets a new benchmark in the quality and efficiency of dynamic scene synthesis. The implications for both theoretical research and practical applications are profound, charting a promising trajectory for the evolution of novel view synthesis methodologies.