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DP2PNet: Diffusion-Based Point-to-Polygon Conversion for Single-Point Supervised Oriented Object Detection.
Peng Li1, Limin Zhang1, Tao Qu2
1Beijing Research Institute of Telemetry, Beijing 100076, China.
Sensors (Basel, Switzerland)
|January 10, 2026
Summary
We introduce DP2PNet, a novel diffusion model for oriented object detection using single-point supervision. This method reduces annotation costs and improves accuracy without complex priors.
Area of Science:
- Computer Vision
- Machine Learning
- Deep Learning
Background:
- Annotating Rotated Bounding Boxes (RBBs) for oriented object detection is resource-intensive.
- Single-point supervision offers efficiency but lacks crucial size and orientation data, necessitating complex model priors.
Purpose of the Study:
- To propose DP2PNet, the first diffusion model framework for single-point supervised oriented object detection.
- To overcome limitations of existing methods by reducing reliance on complex priors and fixed refinement stages.
Main Methods:
- DP2PNet utilizes a multi-scale consistent noise generator, replacing manual priors with Gaussian noise.
- A Noise Cross-Constraint module employs multi-instance learning for optimal noise point selection.
- A Semantic Key Point Aggregator forms pseudo-RBBs from aggregated noise points via graph convolution and convex hulls.
Main Results:
- DP2PNet achieves competitive mAP50 scores of 53.82% on DOTA-v1.0 and 53.61% on DIOR-R.
- The framework demonstrates robustness to noise and strong cross-dataset generalization capabilities.
- Dynamic adjustment of refinement stages is supported without requiring retraining.
Conclusions:
- DP2PNet presents an effective and flexible approach to single-point supervised oriented object detection.
- The diffusion model framework significantly reduces annotation labor while maintaining high performance.
- This method offers a promising direction for cost-effective and robust object detection.
