With the rapid development of intelligent vehicle technology, 3D object detection and tracking play a crucial role in the field of autonomous driving. This article deeply studies the point cloud and image fusion 3D object detection and tracking algorithm in the field of autonomous driving. Laser radar and surround view camera are used as perception sensors, and based on deep learning theory and methods, the focus is on overcoming the difficulties of multi-modal data fusion in 3D detection and tracking under complex traffic conditions. This article proposes a series of innovative algorithms, including MaskSensing algorithm based on image instance segmentation, DeformFusion algorithm based on Transformer architecture, MixFusion algorithm with hybrid fusion strategy, and DeepTrack3D algorithm. These algorithms have achieved significant results on datasets such as nuScenes, effectively improving the accuracy and robustness of 3D object detection and tracking. In the future, further research is needed in areas such as temporal fusion, interactive fusion, and unsupervised learning to enhance the performance of autonomous driving technology.

Chuying Lu. Research on Intelligent 3D Reconstruction System Integrating Transformer and Adaptive Point Cloud Registration. International Journal of Big Data Intelligent Technology (2025), Vol. 6, Issue 1: 101-108. https://doi.org/10.38007/IJBDIT.2025.060110.

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