  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.
