Recent advancements in neural rendering have excelled at novel view synthesis from multi-view RGB images. However, they often lack the capability to edit the shading or colour of the scene at a detailed point-level, while ensuring consistency across different viewpoints. In this work, we address the challenge of point-level 3D scene albedo and shading editing from multi-view RGB images, focusing on detailed editing at the point-level rather than at a part or global level. While prior works based on volumetric representation such as NeRF struggle with achieving 3D consistent editing at the point level, recent advancements in point-based neural rendering show promise in overcoming this challenge. We introduce ``Intrinsic PAPR'', a novel method based on the recent point-based neural rendering technique Proximity Attention Point Rendering (PAPR). Unlike other point-based methods that model the intrinsic decomposition of the scene, our approach does not rely on complicated shading models or simplistic priors that may not universally apply. Instead, we directly model scene decomposition into albedo and shading components, leading to better estimation accuracy. Comparative evaluations against the latest point-based inverse rendering methods demonstrate that Intrinsic PAPR achieves higher-quality novel view rendering and superior point-level albedo and shading editing.

翻译：近年来，神经渲染技术在基于多视角RGB图像的新视角合成方面取得了显著进展。然而，这些方法通常缺乏在确保不同视角间一致性的前提下，对场景的光照或颜色进行精细点级别编辑的能力。本研究致力于解决从多视角RGB图像实现点级别三维场景反照率与光照编辑的挑战，重点关注点级别的精细编辑，而非部件或全局级别的调整。虽然基于体积表示（如NeRF）的先前方法难以实现点级别的三维一致编辑，但基于点的神经渲染技术的最新进展显示出克服这一挑战的潜力。我们提出了"Intrinsic PAPR"——一种基于近期点渲染技术"邻近注意力点渲染（PAPR）"的创新方法。与其他对场景进行本征分解建模的点云方法不同，我们的方法不依赖于可能无法普遍适用的复杂光照模型或简化先验。相反，我们直接对场景分解为反照率与光照分量进行建模，从而获得更优的估计精度。与最新基于点的逆向渲染方法的对比评估表明，Intrinsic PAPR能够实现更高质量的新视角渲染效果，并在点级别的反照率与光照编辑方面表现更为优异。