Tackling Misattribution in 3D Intrinsic Decomposition via Proximity Attention Point Rendering

Recent point-based intrinsic decomposition and inverse ren-dering methods have advanced the modelling of the shading and albedoof 3D scenes. However, we identify a fundamental limitation: these meth-ods suffer from a misattribution issue, where individual primitives learnincorrect appearance features despite producing correct aggregated ren-derings. We show that the root cause lies in volume rendering, whichaggregates translucent primitives along each ray and only supervises thefinal colour, preventing direct supervision of individual primitive fea-tures. To address this, we propose Intrinsic PAPR, a robust intrinsic de-composition framework which leverages Proximity Attention Point Ren-dering (PAPR) to enable direct per-point supervision. Unlike volumerendering approaches, PAPR eliminates translucent primitives and di-rectly predicts appearance at ray-surface intersections, enabling accuratesupervision to the feature of each individual point. Our method incor-porates a 2D albedo prior adapted with conditional Implicit MaximumLikelihood Estimation (cIMLE) to handle monocular ambiguities, andemploys a space carving loss to ensure multi-view consistency. Exten-sive evaluations on synthetic and real-world datasets demonstrate thatIntrinsic PAPR outperforms point-based inverse rendering, NeRF-basedintrinsic decomposition, and diffusion-based PBR methods in novel viewsynthesis and albedo estimation while resolving the misattribution issue.