GitNet: Geometric Prior-Based Transformation for Birds-Eye-View Segmentation

نویسندگان

چکیده

Birds-eye-view (BEV) semantic segmentation is critical for autonomous driving its powerful spatial representation ability. It challenging to estimate the BEV maps from monocular images due gap, since it implicitly required realize both perspective-to-BEV transformation and segmentation. We present a novel two-stage Geometry PrIor-based Transformation framework named GitNet, consisting of (i) geometry-guided pre-alignment (ii) ray-based transformer. In first stage, we decouple into perspective image geometric prior-based mapping, with explicit supervision by projecting labels onto plane learn visibility-aware features learnable geometry translate space. Second, pre-aligned coarse are further deformed transformers take visibility knowledge account. GitNet achieves leading performance on nuScenes Argoverse Datasets.

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ژورنال

عنوان ژورنال: Lecture Notes in Computer Science

سال: 2022

ISSN: ['1611-3349', '0302-9743']

DOI: https://doi.org/10.1007/978-3-031-19769-7_23