2024/05/07 by Jinke Li, Xiao He, Li, Jinke +9 · 12 citations
Computer Science · Earth and Planetary Sciences · Engineering · Neuroscience · Psychology · #3D Surveying and Cultural Heritage #Architectural engineering #Computer Graphics and Visualization Techniques #Computer science #Electrical engineering #Engineering #Neuroscience #Occupancy #Perception #Psychology #Transformer #Video Surveillance and Tracking Methods #Voltage
paper · pdf · doi:10.48550/arxiv.2405.04299
published in arXiv (Cornell University) (Cornell University)
openalex publication_date 2024/05/07 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
3D occupancy, an advanced perception technology for driving scenarios, represents the entire scene without distinguishing between foreground and background by quantifying the physical space into a grid map. The widely adopted projection-first deformable attention, efficient in transforming image features into 3D representations, encounters challenges in aggregating multi-view features due to sensor deployment constraints. To address this issue, we propose our learning-first view attention mechanism for effective multi-view feature aggregation. Moreover, we showcase the scalability of our view attention across diverse multi-view 3D tasks, including map construction and 3D object detection. Leveraging the proposed view attention as well as an additional multi-frame streaming temporal attention, we introduce ViewFormer, a vision-centric transformer-based framework for spatiotemporal feature aggregation. To further explore occupancy-level flow representation, we present FlowOcc3D, a benchmark built on top of existing high-quality datasets. Qualitative and quantitative analyses on this benchmark reveal the potential to represent fine-grained dynamic scenes. Extensive experiments show that our approach significantly outperforms prior state-of-the-art methods. The codes are available at \urlhttps://github.com/ViewFormerOcc/ViewFormer-Occ.