标题： 内在现实：将内在任何东西从合成对象适应到真实对象 显示英文标题

标题： IntrinsicReal: Adapting IntrinsicAnything from Synthetic to Real Objects

摘要： 从在真实环境中捕获的单个RGB图像（例如MVImgNet数据集）中估计反照率（也称为内在图像分解）由于缺乏配对图像及其真实反照率而面临重大挑战。 因此，尽管最近的方法（例如IntrinsicAnything）通过利用强大的扩散先验取得了突破，但它们主要在大规模合成数据集（例如Objaverse）上进行训练，并直接应用于真实世界的RGB图像，这忽略了合成数据和真实数据之间的巨大领域差距，导致泛化性能不佳。 在本工作中，我们通过提出IntrinsicReal，一种新颖的领域自适应框架来解决这一差距，该框架为真实世界的内在图像分解弥合了上述领域差距。 具体来说，我们的IntrinsicReal通过使用一种新的双伪标签策略选择的高质量输出反照率对IntrinsicAnything进行微调，从而适应真实领域：i）基于分类器预测的绝对置信度阈值进行伪标签标注，以及ii）使用分类器预测对于单个输入对象的相对偏好排名进行伪标签标注。 这种策略受到人类评估的启发，其中识别最高质量的输出是简单的，但对于次优情况，绝对分数变得不太可靠。 在这些情况下，输出的相对比较会更加准确。 为了实现这一点，我们提出了一种新的两阶段流程，依次应用这些伪标签技术，以有效地将IntrinsicAnything适应到真实领域。 实验结果表明，我们的IntrinsicReal显著优于现有方法，在合成和真实世界数据集上的反照率估计方面达到了最先进的结果。 显示英文摘要

摘要： Estimating albedo (a.k.a., intrinsic image decomposition) from single RGB images captured in real-world environments (e.g., the MVImgNet dataset) presents a significant challenge due to the absence of paired images and their ground truth albedos. Therefore, while recent methods (e.g., IntrinsicAnything) have achieved breakthroughs by harnessing powerful diffusion priors, they remain predominantly trained on large-scale synthetic datasets (e.g., Objaverse) and applied directly to real-world RGB images, which ignores the large domain gap between synthetic and real-world data and leads to suboptimal generalization performance. In this work, we address this gap by proposing IntrinsicReal, a novel domain adaptation framework that bridges the above-mentioned domain gap for real-world intrinsic image decomposition. Specifically, our IntrinsicReal adapts IntrinsicAnything to the real domain by fine-tuning it using its high-quality output albedos selected by a novel dual pseudo-labeling strategy: i) pseudo-labeling with an absolute confidence threshold on classifier predictions, and ii) pseudo-labeling using the relative preference ranking of classifier predictions for individual input objects. This strategy is inspired by human evaluation, where identifying the highest-quality outputs is straightforward, but absolute scores become less reliable for sub-optimal cases. In these situations, relative comparisons of outputs become more accurate. To implement this, we propose a novel two-phase pipeline that sequentially applies these pseudo-labeling techniques to effectively adapt IntrinsicAnything to the real domain. Experimental results show that our IntrinsicReal significantly outperforms existing methods, achieving state-of-the-art results for albedo estimation on both synthetic and real-world datasets.