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SuPrNet: Super Proxy for 4D occupancy forecasting
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AI
DOI：10.1016/j.knosys.2024.112332.png)
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Abstract
En 中文
Spacetime (4D) occupancy forecasting explicitly models the future development of self-driving vehicles (SDVs) themselves and their surrounding environment, which is highly beneficial for their downstream tasks. Currently, using point cloud forecasting as a proxy to self-supervisedly optimize the 4D occupancy forecasting framework has significant application prospects due to its independence from labeled data. However, existing methods have the following shortcomings: (1) Insufficient mining of spatiotemporal information in the scene. (2) Neglecting the extraction of regional deep features. (3) Inadequate learning ability for spatiotemporal predictive learning. Based on this, we propose SuPrNet, constructing a Super Proxy to address the aforementioned issues. Specifically, SuPrNet first embeds the spatial and temporal information of the current frame into point-wise features through a 4D hybrid representation combined with multi-planar and grid features (4DPG). Then, a Hierarchical Encoder-Decoder Module (HED) with sparsity consideration is used to structure the point cloud and extract deep features. Thanks to the structured feature map representation, SuPrNet proceeds to use a spatiotemporal predictive learning module (STL) to implicitly predict future feature maps. Finally, we decode these feature maps to a specific size as the forecasting of future occupancy. The 4DPG, HED, and STL in the model are designed to address the aforementioned issues, and experiments on Nuscene and KITTIOdometry show that our method achieves the best 4D occupancy forecasting and point cloud forecasting accuracy, surpassing previous state-of-the-art models by approximately 30%. Additionally, we have expanded and validated the application prospects of SuPrNet through various downstream tasks. Our project is available at https://github.com/AlanLiangC/4DPCF.
Keywords:
4D occupancy forecasting
Super proxy
Point cloud forecasting
Point cloud
Self-driving vehicles
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