Distance-Aware Tri-Perspective View for Efficient 3D Perception in Autonomous Driving

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dc.contributor.authorTang, Yutaoen_US
dc.contributor.authorZhao, Jigangen_US
dc.contributor.authorQin, Zhengruien_US
dc.contributor.authorQiu, Ruien_US
dc.contributor.authorZhao, Lingyingen_US
dc.contributor.authorRen, Jieen_US
dc.contributor.authorChen, Guangxien_US
dc.contributor.editorChristie, Marcen_US
dc.contributor.editorHan, Ping-Hsuanen_US
dc.contributor.editorLin, Shih-Syunen_US
dc.contributor.editorPietroni, Nicoen_US
dc.contributor.editorSchneider, Teseoen_US
dc.contributor.editorTsai, Hsin-Rueyen_US
dc.contributor.editorWang, Yu-Shuenen_US
dc.contributor.editorZhang, Eugeneen_US
dc.date.accessioned2025-10-07T06:04:01Z
dc.date.available2025-10-07T06:04:01Z
dc.date.issued2025
dc.description.abstractThree-dimensional environmental perception remains a critical bottleneck in autonomous driving, where existing vision-based dense representations face an intractable trade-off between spatial resolution and computational complexity. Current methods, including Bird's Eye View (BEV) and Tri-Perspective View (TPV), apply uniform perception precision across all spatial regions, disregarding the fundamental safety principle that near-field objects demand high-precision detection for collision avoidance while distant objects permit lower initial accuracy. This uniform treatment squanders computational resources and constrains real-time deployment. We introduce Distance-Aware Tri-Perspective View (DA-TPV), a novel framework that allocates computational resources proportional to operational risk. DA-TPV employs a hierarchical dual-plane architecture for each viewing direction: low-resolution planes capture global scene context while high-resolution planes deliver fine-grained perception within safety-critical reaction zones. Through distance-adaptive feature fusion, our method dynamically concentrates processing power where it most directly impacts vehicle safety. Extensive experiments on nuScenes demonstrate that DA-TPV matches or exceeds single high-resolution TPV performance while reducing memory consumption by 26.3% and achieving real-time inference. This work establishes distance-aware perception as a practical paradigm for deploying sophisticated three-dimensional understanding within automotive computational constraints. Code is available at https://github.com/yytang2012/DA-TPVFormer.en_US
dc.description.sectionheadersPoint Clouds & Gaussian Splatting
dc.description.seriesinformationPacific Graphics Conference Papers, Posters, and Demos
dc.identifier.doi10.2312/pg.20251285
dc.identifier.isbn978-3-03868-295-0
dc.identifier.pages10 pages
dc.identifier.urihttps://doi.org/10.2312/pg.20251285
dc.identifier.urihttps://diglib.eg.org/handle/10.2312/pg20251285
dc.publisherThe Eurographics Associationen_US
dc.rightsAttribution 4.0 International License
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectCCS Concepts: Computing methodologies → Computer vision; Artificial intelligence
dc.subjectComputing methodologies → Computer vision
dc.subjectArtificial intelligence
dc.titleDistance-Aware Tri-Perspective View for Efficient 3D Perception in Autonomous Drivingen_US
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