We present a novel single-stage framework, Neural Photon Field (NePF), to
address the ill-posed inverse rendering from multi-view images. Contrary to
previous methods that recover the geometry, material, and illumination in
multiple stages and extract the properties from various multi-layer perceptrons
across different neural fields, we question such complexities and introduce our
method - a single-stage framework that uniformly recovers all properties. NePF
achieves this unification by fully utilizing the physical implication behind
the weight function of neural implicit surfaces and the view-dependent
radiance. Moreover, we introduce an innovative coordinate-based illumination
model for rapid volume physically-based rendering. To regularize this
illumination, we implement the subsurface scattering model for diffuse
estimation. We evaluate our method on both real and synthetic datasets. The
results demonstrate the superiority of our approach in recovering high-fidelity
geometry and visual-plausible material attributes.

我们提出了一种新的单阶段框架，神经光子场（NePF），用于解决多视图图像中的逆渲染问题。与以往的方法相反，我们质疑在多个阶段中恢复几何、材质和光照，并从不同的神经场中提取属性的复杂性，并介绍了一种单阶段框架，统一恢复所有属性。NePF 通过充分利用神经隐式表面权重函数和视角相关辐射的物理含义，实现了这种统一。此外，我们引入了一种创新的基于坐标的快速体积物理渲染照明模型。为了规范这个照明，我们实现了扩散估计的亚表面散射模型。我们在真实和合成数据集上评估了我们的方法。结果表明，我们的方法在恢复高保真几何和可视合理的材料属性方面具有优越性。

NePF：用于单阶反渲染的神经光子场

NePF: Neural Photon Field for Single-Stage Inverse Rendering

Deep learning based food image classification has enabled more accurate
nutrition content analysis for image-based dietary assessment by predicting the
types of food in eating occasion images. However, there are two major obstacles
to apply food classification in real life applications. First, real life food
images are usually heavy-tailed distributed, resulting in severe
class-imbalance issue. Second, it is challenging to train a single-stage (i.e.
end-to-end) framework under heavy-tailed data distribution, which cause the
over-predictions towards head classes with rich instances and under-predictions
towards tail classes with rare instance. In this work, we address both issues
by introducing a novel single-stage heavy-tailed food classification framework.
Our method is evaluated on two heavy-tailed food benchmark datasets, Food101-LT
and VFN-LT, and achieves the best performance compared to existing work with
over 5% improvements for top-1 accuracy.

本研究介绍了一种新型的单阶段重尾型饮食分类框架以解决实际应用中的类不平衡问题，应用于 Food101-LT 和 VFN-LT 两个基准数据集，取得了超过 5% 的 top-1 准确度的改善。