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ParkOcc: A Novel Dataset and Benchmark for Surround-View Fisheye 3-D Semantic Occupancy Prediction in Automated Parking Scenarios
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DOI：10.1109/tits.2026.3704370.png)
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Abstract
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3D semantic occupancy prediction plays a vital role in fully understanding the surroundings, which has great potential in automated valet parking. Its comprehensive foreground and background awareness is well-suited for parking scenarios, which contain numerous background obstacles such as walls and pillars. During parking, the ego vehicle is often very close to these obstacles, requiring 3D fine-grained modeling of the surroundings. Therefore, considering that mass-produced vehicles are typically equipped with surround-view fisheye cameras, applying the occupancy prediction model based on fisheye cameras to parking perception is significant and promising. However, most existing works focus on applications in urban or highway scenarios, with insufficient attention given to parking scenarios. There are no available datasets or methods exploring the application of occupancy solutions in parking scenarios. To address this, we introduce a new surround-view fisheye occupancy dataset and benchmark called ParkOcc to promote research in dealing with diverse real-world parking cases. In addition, we also propose our new model, AdaptiveOcc v2, which utilizes joint forward and backward projection along with 2D and 3D auxiliary branches to achieve accurate performance. Extensive experiments validate the effectiveness and exceptional generalizability of our approach. We hope the ParkOcc benchmark will boost the development of surrounding occupancy perception algorithms. Code and dataset are available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/yty-sky/ParkOcc</uri>
Keywords:
3D semantic occupancy prediction
automated parking
surround-view fisheye cameras
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