# CompenHR: Efficient Full Compensation for High-resolution Projector

arXiv: https://arxiv.org/abs/2311.13409 · HTML: https://mlsanity.com/paper/2311.13409/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 77 references that are in the map.
- First posted on arXiv 22 Nov 2023.

## Builds on (most cited first, 17 of 17)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification](https://mlsanity.com/paper/1502.01852/) (2015)
- [CBAM: Convolutional Block Attention Module](https://mlsanity.com/paper/1807.06521/) (2018)
- [Image Super-Resolution Using Very Deep Residual Channel Attention Networks](https://mlsanity.com/paper/1807.02758/) (2018)
- [Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network](https://mlsanity.com/paper/1609.05158/) (2016)
- [Lightweight Image Super-Resolution with Information Multi-distillation Network](https://mlsanity.com/paper/1909.11856/) (2019)
- [SCA-CNN: Spatial and Channel-wise Attention in Convolutional Networks for Image Captioning](https://mlsanity.com/paper/1611.05594/) (2016)
- [Efficient Image Super-Resolution Using Pixel Attention](https://mlsanity.com/paper/2010.01073/) (2020)
- [Squeeze-and-Attention Networks for Semantic Segmentation](https://mlsanity.com/paper/1909.03402/) (2019)
- [Residual Local Feature Network for Efficient Super-Resolution](https://mlsanity.com/paper/2205.07514/) (2022)
- [Multi-Attention Based Ultra Lightweight Image Super-Resolution](https://mlsanity.com/paper/2008.12912/) (2020)
- [End-to-end Full Projector Compensation](https://mlsanity.com/paper/2008.00965/) (2020)
- [End-to-end Projector Photometric Compensation](https://mlsanity.com/paper/1904.04335/) (2019)
- [CompenNet++: End-to-end Full Projector Compensation](https://mlsanity.com/paper/1908.06246/) (2019)
- [IlluminatedFocus: Vision Augmentation using Spatial Defocusing via Focal Sweep Eyeglasses and High-Speed Projector](https://mlsanity.com/paper/2002.02167/) (2020)

## Built on by (most cited first, 6 of 6)

- [GS-ProCams: Gaussian Splatting-based Projector-Camera Systems](https://mlsanity.com/paper/2412.11762/) (2024)
- [DPCS: Path Tracing-Based Differentiable Projector-Camera Systems](https://mlsanity.com/paper/2503.12174/) (2025)
- [LAPIG: Language Guided Projector Image Generation with Surface Adaptation and Stylization](https://mlsanity.com/paper/2503.12173/) (2025)
- [ProCap: Projection-Aware Captioning for Spatial Augmented Reality](https://mlsanity.com/paper/2604.00912/) (2026)
- [Setup-Independent Full Projector Compensation](https://mlsanity.com/paper/2604.01736/) (2026)
- [Physically-guided Image Generation for Multi-Projection Mapping](https://mlsanity.com/paper/2606.22477/) (2026)

## Abstract (arXiv)

Full projector compensation is a practical task of projector-camera systems. It aims to find a projector input image, named compensation image, such that when projected it cancels the geometric and photometric distortions due to the physical environment and hardware. State-of-the-art methods use deep learning to address this problem and show promising performance for low-resolution setups. However, directly applying deep learning to high-resolution setups is impractical due to the long training time and high memory cost. To address this issue, this paper proposes a practical full compensation solution. Firstly, we design an attention-based grid refinement network to improve geometric correction quality. Secondly, we integrate a novel sampling scheme into an end-to-end compensation network to alleviate computation and introduce attention blocks to preserve key features. Finally, we construct a benchmark dataset for high-resolution projector full compensation. In experiments, our method demonstrates clear advantages in both efficiency and quality.