DOI: 10.7763/IJCTE.2026.V18.1387
Hybrid 3D Object Detection in Logistic Environments Using a Histogram Depth Filter
2. School of Engineering and Design, Technical University of Munich, Germany
Email: daniel.vidal@tum.de (D.V.); j.fottner@tum.de (J.F.); boyan.liu@tum.de (B.L.)
*Corresponding author
Manuscript received February 13, 2025; revised August 25, 2025; accepted November 21, 2025; published February 25, 2026
Abstract—In intralogistics, accurate 3D object detection is essential for enhancing robotic perception and enabling autonomous navigation. This paper presents a novel hybrid method that combines 2D object detection with depth-based point cloud segmentation for efficient and real-time 3D object pose estimation. The key contribution of this paper is the Cumulative Histogram Depth Filter (HDF), a lightweight algorithm that segments dominant depth regions corresponding to detected objects. This approach uniquely enables the reuse of existing 2D-labeled datasets, eliminating the need for extensive 3D annotations. The proposed method was evaluated using both simulated and real-world data, obtaining high detection accuracy. In the simulated environment, it achieved a mean distance error of 0.14 m, an Intersection over Union (IoU) of 0.90, and a mean Average Precision at 50 (mAP@50) of 0.95. Real-world experiments using Azure Kinect and ZED2 cameras yielded an average distance error of 0.13 m, IoU of 0.78, and mAP@50 of 0.65. Additionally, the system runs at 105 FPS, significantly outperforming more complex hybrid architectures in terms of computational efficiency, making it particularly suitable for real-time robotic applications.
Keywords—hybrid 3D object detection, autonomous mobile robots, intralogistics, histogram depth filter, YOLO11, synthetic data
Cite: Daniel Vidal, Johannes Fottner, and Boyan Liu, "Hybrid 3D Object Detection in Logistic Environments Using a Histogram Depth Filter," International Journal of Computer Theory and Engineering, vol. 18, no. 1, pp. 38-47, 2026.
Copyright © 2026 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).