Setup-Independent Full Projector Compensation

2026-04-02

AI Summary

Existing projection compensation methods struggle to generalize due to their reliance on specific setups and are highly susceptible to geometric and photometric distortions on non-planar or textured surfaces. This work proposes SIComp, the first general-purpose compensation framework capable of adapting to unseen projector–camera configurations without retraining or fine-tuning. SIComp achieves this by decoupling geometric and photometric compensation within a co-adaptive architecture: a tailored optical flow module enables online geometric correction, while a novel photometric network integrates intensity-aware surface priors to enhance illumination robustness. Evaluated on a large-scale dataset encompassing 277 real-world configurations, SIComp significantly outperforms existing approaches across diverse unseen scenarios, establishing the first truly generalizable solution for projection compensation.

Abstract

Projector compensation seeks to correct geometric and photometric distortions that occur when images are projected onto nonplanar or textured surfaces. However, most existing methods are highly setup-dependent, requiring fine-tuning or retraining whenever the surface, lighting, or projector-camera pose changes. Progress has been limited by two key challenges: (1) the absence of large, diverse training datasets and (2) existing geometric correction models are typically constrained by specific spatial setups; without further retraining or fine-tuning, they often fail to generalize directly to novel geometric configurations. We introduce SIComp, the first Setup-Independent framework for full projector Compensation, capable of generalizing to unseen setups without fine-tuning or retraining. To enable this, we construct a large-scale real-world dataset spanning 277 distinct projector-camera setups. SIComp adopts a co-adaptive design that decouples geometry and photometry: A carefully tailored optical flow module performs online geometric correction, while a novel photometric network handles photometric compensation. To further enhance robustness under varying illumination, we integrate intensity-varying surface priors into the network design. Extensive experiments demonstrate that SIComp consistently produces high-quality compensation across diverse unseen setups, substantially outperforming existing methods in terms of generalization ability and establishing the first generalizable solution to projector compensation. The code and dataset are available on our project page: https://hai-bo-li.github.io/SIComp/

Problem

• projector compensation
• setup-independent
• geometric distortion
• photometric distortion
• generalization

Innovation

• Setup-Independent
• Projector Compensation
• Geometric-Photometric Decoupling
• Optical Flow
• Surface Prior