Papers › Projecting Points to Axes: Oriented Object Detection via Point-Axis Representation

Projecting Points to Axes: Oriented Object Detection via Point-Axis Representation

11 Jul 2024arXiv:2407.08489archive 2025-07-28

Zeyang Zhao, Qilong Xue, Yuhang He, Yifan Bai, Xing Wei, Yihong Gong

This paper introduces the point-axis representation for oriented object detection, emphasizing its flexibility and geometrically intuitive nature with two key components: points and axes. 1) Points delineate the spatial extent and contours of objects, providing detailed shape descriptions. 2) Axes define the primary directionalities of objects, providing essential orientation cues crucial for precise detection. The point-axis representation decouples location and rotation, addressing the loss discontinuity issues commonly encountered in traditional bounding box-based approaches. For effective optimization without introducing additional annotations, we propose the max-projection loss to supervise point set learning and the cross-axis loss for robust axis representation learning. Further, leveraging this representation, we present the Oriented DETR model, seamlessly integrating the DETR framework for precise point-axis prediction and end-to-end detection. Experimental results demonstrate significant performance improvements in oriented object detection tasks.

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Code

zhaozeyang108/Oriented-DETR mentioned on GitHubpytorch report

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Tasks

Object DetectionObject Detection In Aerial ImagesOriented Object DetectionRepresentation Learningobject-detection

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Object Detection In Aerial Images DOTA Oriented-DETR mAP 82.26% #4 of 58 Archive leaderboard report

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Methods

Absolute Position EncodingsAdamAttentionBPEConvolutionDense ConnectionsDetrDropoutFeedforward NetworkLabel SmoothingLayer NormalizationLinear LayerMulti-Head AttentionPosition-Wise Feed-Forward LayerResidual ConnectionSETSoftmaxTransformer

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