  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.
