Unified Spatio-Temporal Tri-Perspective View Representation for 3D Semantic Occupancy Prediction

• URL: http://arxiv.org/abs/2401.13785v2
• Date: Thu, 4 Apr 2024 13:52:17 GMT
• Title: Unified Spatio-Temporal Tri-Perspective View Representation for 3D Semantic Occupancy Prediction
• Authors: Sathira Silva, Savindu Bhashitha Wannigama, Gihan Jayatilaka, Muhammad Haris Khan, Roshan Ragel,
• Abstract summary: This study introduces architecture2TPVFormer for temporally coherent 3D semantic occupancy prediction. We enrich the prior process by including temporal cues using a novel temporal cross-view hybrid attention mechanism. Experimental evaluations demonstrate a substantial 4.1% improvement in mean Intersection over Union for 3D Semantic Occupancy.
• Score: 6.527178779672975
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Holistic understanding and reasoning in 3D scenes play a vital role in the success of autonomous driving systems. The evolution of 3D semantic occupancy prediction as a pretraining task for autonomous driving and robotic downstream tasks capture finer 3D details compared to methods like 3D detection. Existing approaches predominantly focus on spatial cues such as tri-perspective view embeddings (TPV), often overlooking temporal cues. This study introduces a spatiotemporal transformer architecture S2TPVFormer for temporally coherent 3D semantic occupancy prediction. We enrich the prior process by including temporal cues using a novel temporal cross-view hybrid attention mechanism (TCVHA) and generate spatiotemporal TPV embeddings (i.e. S2TPV embeddings). Experimental evaluations on the nuScenes dataset demonstrate a substantial 4.1% improvement in mean Intersection over Union (mIoU) for 3D Semantic Occupancy compared to TPVFormer, confirming the effectiveness of the proposed S2TPVFormer in enhancing 3D scene perception.

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