{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T07:00:32Z","timestamp":1791874832953,"version":"4.3.4"},"reference-count":39,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2025,5,26]],"date-time":"2025-05-26T00:00:00Z","timestamp":1748217600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Hunan Provincial Natural Science Foundation of China","award":["2024JJ7428"],"award-info":[{"award-number":["2024JJ7428"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>This paper presents a bidirectional feature fusion network (BiDFNet) for 3D object detection, leveraging pseudo-point clouds to achieve bidirectional fusion of point cloud and image features. The proposed model addresses key challenges in multimodal 3D detection by introducing three novel components: (1) the SAF-Conv module, which extends the receptive field through improved submanifold sparse convolution, enhancing feature extraction from pseudo-point clouds while effectively reducing edge noise; (2) the bidirectional cross-modal attention feature interaction module (BiCSAFIM), which employs a multi-head cross-attention mechanism to enable global information interaction between point cloud and image features; and (3) the attention-based feature fusion module (ADFM), which adaptively fuses dual-stream features to improve robustness. Extensive experiments on the KITTI dataset demonstrate that BiDFNet achieves state-of-the-art performance, with a 3D AP (R40) of 88.79% on the validation set and 85.27% on the test set for the Car category, significantly outperforming existing methods. These results highlight the effectiveness of BiDFNet in complex scenarios, showcasing its potential for real-world applications such as autonomous driving.<\/jats:p>","DOI":"10.3390\/info16060437","type":"journal-article","created":{"date-parts":[[2025,5,27]],"date-time":"2025-05-27T11:12:57Z","timestamp":1748344377000},"page":"437","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["BiDFNet: A Bidirectional Feature Fusion Network for 3D Object Detection Based on Pseudo-LiDAR"],"prefix":"10.3390","volume":"16","author":[{"given":"Qiang","family":"Zhu","sequence":"first","affiliation":[{"name":"School of Computer Science, University of South China, Hengyang 421001, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yaping","family":"Wan","sequence":"additional","affiliation":[{"name":"School of Computer Science, University of South China, Hengyang 421001, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,5,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Zhou, Y., and Tuzel, O. 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