Inverse++: Vision-Centric 3D Semantic Occupancy Prediction Assisted with 3D Object Detection
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866916676177494016 |
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| author | Ming, Zhenxing Berrio, Julie Stephany Shan, Mao Worrall, Stewart |
| author_facet | Ming, Zhenxing Berrio, Julie Stephany Shan, Mao Worrall, Stewart |
| contents | 3D semantic occupancy prediction aims to forecast detailed geometric and semantic information of the surrounding environment for autonomous vehicles (AVs) using onboard surround-view cameras. Existing methods primarily focus on intricate inner structure module designs to improve model performance, such as efficient feature sampling and aggregation processes or intermediate feature representation formats. In this paper, we explore multitask learning by introducing an additional 3D supervision signal by incorporating an additional 3D object detection auxiliary branch. This extra 3D supervision signal enhances the model's overall performance by strengthening the capability of the intermediate features to capture small dynamic objects in the scene, and these small dynamic objects often include vulnerable road users, i.e. bicycles, motorcycles, and pedestrians, whose detection is crucial for ensuring driving safety in autonomous vehicles. Extensive experiments conducted on the nuScenes datasets, including challenging rainy and nighttime scenarios, showcase that our approach attains state-of-the-art results, achieving an IoU score of 31.73% and a mIoU score of 20.91% and excels at detecting vulnerable road users (VRU). The code will be made available at:https://github.com/DanielMing123/Inverse++ |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_04732 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Inverse++: Vision-Centric 3D Semantic Occupancy Prediction Assisted with 3D Object Detection Ming, Zhenxing Berrio, Julie Stephany Shan, Mao Worrall, Stewart Computer Vision and Pattern Recognition Robotics 3D semantic occupancy prediction aims to forecast detailed geometric and semantic information of the surrounding environment for autonomous vehicles (AVs) using onboard surround-view cameras. Existing methods primarily focus on intricate inner structure module designs to improve model performance, such as efficient feature sampling and aggregation processes or intermediate feature representation formats. In this paper, we explore multitask learning by introducing an additional 3D supervision signal by incorporating an additional 3D object detection auxiliary branch. This extra 3D supervision signal enhances the model's overall performance by strengthening the capability of the intermediate features to capture small dynamic objects in the scene, and these small dynamic objects often include vulnerable road users, i.e. bicycles, motorcycles, and pedestrians, whose detection is crucial for ensuring driving safety in autonomous vehicles. Extensive experiments conducted on the nuScenes datasets, including challenging rainy and nighttime scenarios, showcase that our approach attains state-of-the-art results, achieving an IoU score of 31.73% and a mIoU score of 20.91% and excels at detecting vulnerable road users (VRU). The code will be made available at:https://github.com/DanielMing123/Inverse++ |
| title | Inverse++: Vision-Centric 3D Semantic Occupancy Prediction Assisted with 3D Object Detection |
| topic | Computer Vision and Pattern Recognition Robotics |
| url | https://arxiv.org/abs/2504.04732 |