ColNeRF: Collaboration for Generalizable Sparse Input Neural Radiance Field
(2312.09095)Abstract
Neural Radiance Fields (NeRF) have demonstrated impressive potential in synthesizing novel views from dense input, however, their effectiveness is challenged when dealing with sparse input. Existing approaches that incorporate additional depth or semantic supervision can alleviate this issue to an extent. However, the process of supervision collection is not only costly but also potentially inaccurate, leading to poor performance and generalization ability in diverse scenarios. In our work, we introduce a novel model: the Collaborative Neural Radiance Fields (ColNeRF) designed to work with sparse input. The collaboration in ColNeRF includes both the cooperation between sparse input images and the cooperation between the output of the neural radiation field. Through this, we construct a novel collaborative module that aligns information from various views and meanwhile imposes self-supervised constraints to ensure multi-view consistency in both geometry and appearance. A Collaborative Cross-View Volume Integration module (CCVI) is proposed to capture complex occlusions and implicitly infer the spatial location of objects. Moreover, we introduce self-supervision of target rays projected in multiple directions to ensure geometric and color consistency in adjacent regions. Benefiting from the collaboration at the input and output ends, ColNeRF is capable of capturing richer and more generalized scene representation, thereby facilitating higher-quality results of the novel view synthesis. Extensive experiments demonstrate that ColNeRF outperforms state-of-the-art sparse input generalizable NeRF methods. Furthermore, our approach exhibits superiority in fine-tuning towards adapting to new scenes, achieving competitive performance compared to per-scene optimized NeRF-based methods while significantly reducing computational costs. Our code is available at: https://github.com/eezkni/ColNeRF.
Overview
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ColNeRF efficiently handles sparse input data for Neural Radiance Field models without additional supervision, improving view synthesis.
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By introducing a collaborative module and self-supervised constraints, ColNeRF ensures cross-view consistency.
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The Volume Integration module included helps to manage complex occlusions and deduce spatial object locations.
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Experimental results show ColNeRF outperforms other NeRF methods on sparse inputs and is effective in scene adaptation.
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This advancement in NeRF technology is significant for applications that have limitations on input data and computational resources.
Introduction
Neural Radiance Fields (NeRF) have shown impressive capabilities in novel view synthesis, creating new images of a scene from various viewpoints. This technology has clear implications for fields such as virtual reality, autonomous driving, and robotics. However, traditional NeRF models require densely sampled input images, which can be challenging and impractical to obtain. While existing models struggle to maintain effectiveness and generalization with sparse inputs, the paper introduces a model called Collaborative Neural Radiance Fields (ColNeRF), aiming to tackle these challenges without the need for additional supervision.
Collaborative Neural Radiance Fields (ColNeRF)
ColNeRF's strategy relies on leveraging the sparse input by encouraging collaboration both at the input and output stages. The model incorporates a novel collaborative module which aligns information from different views and reinforces self-supervised constraints to ensure consistency across those views. It also introduces a Volume Integration module that captures complex occlusions and infers spatial object locations implicitly.
Furthermore, to improve geometry and appearance reconstruction, ColNeRF employs self-supervised target ray projection in multiple directions to enforce collaboration. This method allows for richer scene representation, improving the quality of synthesized novel views. The model has shown the ability to adapt to new scenes with competitive performance compared to specific NeRF-based methods, but with significantly reduced computational effort.
Experimental Results
Experiments conducted using the DTU and LLFF datasets demonstrate that ColNeRF outperforms state-of-the-art generalizable NeRF methods in scenarios with sparse inputs. The method achieves superior results especially in fine-tuning for scene adaptation. Even in setups with as few as 3 input views, ColNeRF was able to produce high-quality results. The comprehensive tests and comparisons support the model's capabilities in maintaining geometric and visual fidelity.
Conclusion
ColNeRF's introduction marks a notable advancement in NeRF research, particularly when dealing with sparse input data where traditional methods fall short. By integrating features effectively across different views and outputting consistent geometry and appearance, ColNeRF creates more accurate 3D models and color-consistent renderings without requiring external supervision. This model opens up new possibilities for resource-efficient and generalized application of neural radiance fields technology in real-world scenarios. Future work in this field can further refine the model to speed up the process and detail improvements even more.
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