Transfer Learning from Simulated to Real Scenes for Monocular 3D Object Detection

• URL: http://arxiv.org/abs/2408.15637v1
• Date: Wed, 28 Aug 2024 08:44:58 GMT
• Title: Transfer Learning from Simulated to Real Scenes for Monocular 3D Object Detection
• Authors: Sondos Mohamed, Walter Zimmer, Ross Greer, Ahmed Alaaeldin Ghita, Modesto Castrillón-Santana, Mohan Trivedi, Alois Knoll, Salvatore Mario Carta, Mirko Marras,
• Abstract summary: 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. We fine-tune the model on a combination of real-world datasets to enhance its adaptability to practical conditions.
• Score: 9.708971995966476
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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