Utilizing monocular cameras for 3D object understanding is widely recognized as a cost-effective approach, spanning applications such as autonomous driving, augmented/virtual reality or roadside monitoring. Despite recent progress, persistent challenges arise in creating generalized models adaptable to unforeseen scenarios and diverse camera configurations. In this work, we focus on the task of monocular 3D object detection within roadside environments. To begin, we introduce a versatile methodology for generating and labeling datasets tailored to roadside scenarios, addressing limitations encountered in real-world settings. Subsequently, we develop an array of deep learning models tailored to this task, refining them to address practical challenges that emerge during real-world application. Lastly, leveraging our framework, we curated a synthetic benchmark dataset comprising 1,415,680 frames and 8,902,636 labeled 3D objects, ultimately assessing the performance of existing models across all datasets.

Accurately detecting 3D objects from monocular images in dynamic roadside scenarios remains a challenging problem due to varying camera perspectives and unpredictable scene conditions. This paper introduces a two-stage training strategy to address these challenges. Our approach initially trains a model on the large-scale synthetic dataset, RoadSense3D, which offers a diverse range of scenarios for robust feature learning. Subsequently, we fine-tune the model on a combination of real-world datasets to enhance its adaptability to practical conditions. Experimental results of the Cube R-CNN model on challenging public benchmarks show a remarkable improvement in detection performance, with a mean average precision rising from 0.26 to 12.76 on the TUM Traffic A9 Highway dataset and from 2.09 to 6.60 on the DAIR-V2X-I dataset when performing transfer learning. Code, data, and qualitative video results are available on the project website: https://roadsense3d.github.io.