AI 中文总结
针对传统蒙特卡洛样本方差衡量逐像素渲染难度可靠性低的问题，提出基于传输事件分类的离散描述符，稳定性显著优于方差，可用于优化样本分配并具备分布外泛化能力。
AI 中文摘要
逐像素渲染难度传统上通过蒙特卡洛估计器的样本方差$\boldsymbol{\frac{\hatσ^2(p)}{}}$衡量，但该信号恰恰在难度集中的区域最不可靠：在重尾传输下，其相对误差由被积函数的峰度决定，即使在每像素40000个样本的情况下，基于方差的评估目标的分半信度也仅达到0.23-0.29。我们提出了一种互补的离散传输机制描述符：每个贡献事件按其末端顶点的BSDF波瓣、是否存在delta镜面事件以及单/多次反弹的区别进行分类，产生七个互斥标签，其中六个命名机制承载了测试场景中所有观测到的能量，连续旁通通道保留了机制混合比例。在七个场景中，每像素64个样本与4096个样本的主导标签一致性达87-99.6%——而分箱方差的一致性最低仅为21%——且对恢复估计器的MIS（多重重要性采样）部分具有鲁棒性。该描述符揭示了标量方差无法表示的跨场景结构，包括delta介导/光泽相关性的几何控制符号反转。在所有存在重尾区间的测试矩阵场景中，使用该标签校正含噪先导方差的方法，在相同预算下优于先导方差样本分配方法，而在无此类区间的场景中则完全退化为现有方法，其增益在随机分区安慰剂测试中依然存在，且在稳健的（均值中位数）先导基线下仍保持。预注册的第三方哨兵测试证实了该方法的分布外泛化能力：覆盖率和稳定性可迁移，一项结构发现通过了盲符号预测测试；在半开门场景中，先导方差分配方法的表现比均匀采样低6.8 dB，该标签仅通过先导信息就能识别出该故障不属于其可修复的类型，并正确地弃权（不执行）。
英文摘要
Per-pixel rendering difficulty is conventionally characterised by one noisy scalar: the sample variance of a Monte Carlo estimator. We argue that it should instead be characterised through \emph{transport structure} --- a discrete description of how each contribution's energy reaches the sensor, deterministic under stated renderer conventions --- with variance treated as a measurement whose reliability that structure helps predict. We make this concrete with a seven-class transport-mechanism descriptor assigned per contribution event, evaluate it on eleven scenes, and measure variance reliability on the seven first-party ones. The dominant label agrees $87$--$99.6\%$ between 64 and 4096 samples per pixel, where quantile-binned variance agrees as little as $21\%$; its stability on unseen scenes is predicted from their pilots. Conditioning a pilot variance on the label improves equal-budget sample allocation wherever heavy-tailed buckets carry appreciable population, reduces to the incumbent where they are absent or negligible, and is neither reproduced by a random partition nor absorbed by a median-of-means estimator; where the pilot fails for other reasons, as on the classical ajar-door scene ($6.8$~dB below uniform), the label says so from the pilot alone.
