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Holistic understanding and reasoning in 3D scenes are crucial for the successof autonomous driving systems. The evolution of 3D semantic occupancyprediction as a pretraining task for autonomous driving and roboticapplications captures finer 3D details compared to traditional 3D detectionmethods. Vision-based 3D semantic occupancy prediction is increasinglyoverlooked in favor of LiDAR-based approaches, which have shown superiorperformance in recent years. However, we present compelling evidence that thereis still potential for enhancing vision-based methods. Existing approachespredominantly 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 temporallycoherent 3D semantic occupancy. By introducing temporal cues through a novelTemporal Cross-View Hybrid Attention mechanism (TCVHA), we generateSpatiotemporal 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 semanticoccupancy compared to baseline TPVFormer, validating the effectiveness ofS2TPVFormer in advancing 3D scene perception.

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