MonOri: Orientation-Guided PnP for Monocular 3-D Object Detection

作者：Hongdou Yao, Pengfei Han, Jun Chen, Zheng Wang, Yansheng Qiu, Xiao Wang, Yimin Wang, Xiaoyu Chai, Chenglong Cao, Jin Wei · 发表于：IEEE Transactions on Neural Networks and Learning Systems · 年份：2025 · DOI：10.1109/tnnls.2025.3577618 · 被引用次数：4 · 研究领域：Advanced Neural Network Applications、Robotics and Sensor-Based Localization、Advanced Image and Video Retrieval Techniques

Monocular 3-D object detection is a challenging task in the field of autonomous driving and has made great progress. However, current monocular image methods tend to incorporate additional information such as pseudolabels to improve algorithm performance while overlooking the geometric relationship between the object's keypoints, resulting in low performance for occluded object detection. To address this issue, we find that introducing the orientation information of objects in the 3-D detection pipeline can help improve the detection performance of occluded objects. An orientation-guided perspective-n-point (PnP) for monocular 3-D object detection method named MonOri is presented in this article, which uses object's orientation to guide keypoints' optimization. Considering the existence of different deformation objects in the scene, we design the feature aggregation detection module (FADM), which consists of the feature focus fusion module (FFFM) and CondConv detection module (CCDM). First, FFFM can highlight signals from irregularly occluded objects, effectively modeling features of elongated and small-sized objects. This module enhances the model's ability to recognize elongated and small-sized objects in complex scenes. Then, the CCDM is designed to improve the network's ability to estimate object keypoints' location regression under occlusion conditions and minimize the network computational overhead. Finally, considering that the unoccluded portions of occluded objects a...