We propose an improvement to the landmark validity loss. Landmark detection
is widely used in head pose estimation, eyelid shape extraction, as well as
pupil and iris segmentation. There are numerous additional applications where
landmark detection is used to estimate the shape of complex objects. One part
of this process is the accurate and fine-grained detection of the shape. The
other part is the validity or inaccuracy per landmark, which can be used to
detect unreliable areas, where the shape possibly does not fit, and to improve
the accuracy of the entire shape extraction by excluding inaccurate landmarks.
We propose a normalization in the loss formulation, which improves the accuracy
of the entire approach due to the numerical balance of the normalized
inaccuracy. In addition, we propose a margin for the inaccuracy to reduce the
impact of gradients, which are produced by negligible errors close to the
ground truth.

我们提出了一种改进的地标有效性损失，通过归一化来提高准确度，并通过设定不准确性的边界来减少梯度对结果的影响。

基於回歸的眼睛特徵提取中的標準化有效性分數

Normalized Validity Scores for DNNs in Regression based Eye Feature  Extraction

We introduce a new implicit shape representation called Primary Ray-based
Implicit Function (PRIF). In contrast to most existing approaches based on the
signed distance function (SDF) which handles spatial locations, our
representation operates on oriented rays. Specifically, PRIF is formulated to
directly produce the surface hit point of a given input ray, without the
expensive sphere-tracing operations, hence enabling efficient shape extraction
and differentiable rendering. We demonstrate that neural networks trained to
encode PRIF achieve successes in various tasks including single shape
representation, category-wise shape generation, shape completion from sparse or
noisy observations, inverse rendering for camera pose estimation, and neural
rendering with color.

本文提出了一种称为 Primary Ray-based Implicit Function (PRIF) 的新的隐式形状表示方法，能够高效地进行形状的提取和可微渲染，并成功地用于单一形状表示、类别化形状生成、稀疏或噪声观测下的形状补全、相机姿态估计反渲染和神经渲染等各种任务。