ShelfRectNet: Single View Shelf Image Rectification with Homography Estimation

• URL: http://arxiv.org/abs/2511.20335v1
• Date: Tue, 25 Nov 2025 14:14:17 GMT
• Title: ShelfRectNet: Single View Shelf Image Rectification with Homography Estimation
• Authors: Onur Berk Tore, Ibrahim Samil Yalciner, Server Calap,
• Abstract summary: We present a deep learning framework that predicts a 4-point parameterized homography matrix to rectify shelf images captured from arbitrary angles.<n>Our method achieves a mean corner error of 1.298 pixels on the test set.<n>To encourage further research in this domain, we will make our dataset, ShelfRectSet, and code publicly available.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Estimating homography from a single image remains a challenging yet practically valuable task, particularly in domains like retail, where only one viewpoint is typically available for shelf monitoring and product alignment. In this paper, we present a deep learning framework that predicts a 4-point parameterized homography matrix to rectify shelf images captured from arbitrary angles. Our model leverages a ConvNeXt-based backbone for enhanced feature representation and adopts normalized coordinate regression for improved stability. To address data scarcity and promote generalization, we introduce a novel augmentation strategy by modeling and sampling synthetic homographies. Our method achieves a mean corner error of 1.298 pixels on the test set. When compared with both classical computer vision and deep learning-based approaches, our method demonstrates competitive performance in both accuracy and inference speed. Together, these results establish our approach as a robust and efficient solution for realworld single-view rectification. To encourage further research in this domain, we will make our dataset, ShelfRectSet, and code publicly available

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