Neural radiance fields with stochasticity have garnered significant interest by enabling the sampling of plausible radiance fields and quantifying uncertainty for downstream tasks. Existing works rely on the independence assumption of points in the radiance field or the pixels in input views to obtain tractable forms of the probability density function. However, this assumption inadvertently impacts performance when dealing with intricate geometry and texture. In this work, we propose an independence-assumption-free probabilistic neural radiance field based on Flow-GAN. By combining the generative capability of adversarial learning and the powerful expressivity of normalizing flow, our method explicitly models the density-radiance distribution of the whole scene. We represent our probabilistic NeRF as a mean-shifted probabilistic residual neural model. Our model is trained without an explicit likelihood function, thereby avoiding the independence assumption. Specifically, We downsample the training images with different strides and centers to form fixed-size patches which are used to train the generator with patch-based adversarial learning. Through extensive experiments, our method demonstrates state-of-the-art performance by predicting lower rendering errors and more reliable uncertainty on both synthetic and real-world datasets.

翻译：具有随机性的神经辐射场因能够采样合理的辐射场并为下游任务量化不确定性而备受关注。现有工作依赖辐射场中点或输入视图中像素的独立性假设来获得概率密度函数的可处理形式，但这一假设在处理复杂几何结构与纹理时会影响性能。本文提出一种基于Flow-GAN的无独立性假设概率神经辐射场。通过结合对抗学习的生成能力与归一化流的强大表达能力，本方法显式建模了整个场景的密度-辐射分布。我们将概率NeRF表示为均值偏移的概率残差神经模型，该模型无需显式似然函数即可训练，从而避免了独立性假设。具体而言，我们以不同步长和中心对训练图像进行下采样，形成固定大小的图像块，并采用基于图像块的对抗学习训练生成器。大量实验表明，本方法在合成与真实数据集上均能预测更低的渲染误差和更可靠的不确定性，达到了最先进性能。