Implicit functions such as Neural Radiance Fields (NeRFs), occupancy
networks, and signed distance functions (SDFs) have become pivotal in computer
vision for reconstructing detailed object shapes from sparse views. Achieving
optimal performance with these models can be challenging due to the extreme
sparsity of inputs and distribution shifts induced by data corruptions. To this
end, large, noise-free synthetic datasets can serve as shape priors to help
models fill in gaps, but the resulting reconstructions must be approached with
caution. Uncertainty estimation is crucial for assessing the quality of these
reconstructions, particularly in identifying areas where the model is uncertain
about the parts it has inferred from the prior. In this paper, we introduce
Dropsembles, a novel method for uncertainty estimation in tuned implicit
functions. We demonstrate the efficacy of our approach through a series of
experiments, starting with toy examples and progressing to a real-world
scenario. Specifically, we train a Convolutional Occupancy Network on synthetic
anatomical data and test it on low-resolution MRI segmentations of the lumbar
spine. Our results show that Dropsembles achieve the accuracy and calibration
levels of deep ensembles but with significantly less computational cost.

利用大规模的无噪声合成数据集作为形状先验来帮助模型填补缺失信息，同时通过引入 Dropsembles 方法进行不确定性估计，我们在神经辐射场、占据网络和符号距离函数等领域进行了一系列实验来验证其有效性，并在低分辨率 MRI 分割中的腰椎案例中展示了 Dropsembles 方法具有与深度集成相当的准确度和校准度但计算成本显著降低。