A Spatiotemporal Approach to Tri-Perspective Representation for 3D Semantic Occupancy Prediction

• URL: http://arxiv.org/abs/2401.13785v3
• Date: Sun, 16 Feb 2025 10:14:02 GMT
• Title: A Spatiotemporal Approach to Tri-Perspective Representation for 3D Semantic Occupancy Prediction
• Authors: Sathira Silva, Savindu Bhashitha Wannigama, Gihan Jayatilaka, Muhammad Haris Khan, Roshan Ragel,
• Abstract summary: Vision-based 3D semantic occupancy prediction is increasingly overlooked in favor of LiDAR-based approaches.<n>This study introduces S2TPVFormer, a transformer architecture designed to predict temporally coherent 3D semantic occupancy.
• Score: 6.527178779672975
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Holistic understanding and reasoning in 3D scenes are crucial for the success of autonomous driving systems. The evolution of 3D semantic occupancy prediction as a pretraining task for autonomous driving and robotic applications captures finer 3D details compared to traditional 3D detection methods. Vision-based 3D semantic occupancy prediction is increasingly overlooked in favor of LiDAR-based approaches, which have shown superior performance in recent years. However, we present compelling evidence that there is still potential for enhancing vision-based methods. Existing approaches predominantly focus on spatial cues such as tri-perspective view (TPV) embeddings, often overlooking temporal cues. This study introduces S2TPVFormer, a spatiotemporal transformer architecture designed to predict temporally coherent 3D semantic occupancy. By introducing temporal cues through a novel Temporal Cross-View Hybrid Attention mechanism (TCVHA), we generate Spatiotemporal TPV (S2TPV) embeddings that enhance the prior process. Experimental evaluations on the nuScenes dataset demonstrate a significant +4.1% of absolute gain in mean Intersection over Union (mIoU) for 3D semantic occupancy compared to baseline TPVFormer, validating the effectiveness of S2TPVFormer in advancing 3D scene perception.

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