This paper proposes a lightweight yet effective network architecture for depth completion. It enables to fuse multi-modal and multi-level features through a Cascade Dense Connection Fusion Network. This is implemented by means of a dense connection fusion block, multi-scale features and a modality-aware aggregation mechanism. Our model is evaluated on the KITTI benchmark and achieves competitive results compared with state-of-the-art while counting much fewer parameters.

A Cascade Dense Connection Fusion Network for Depth Completion

R. Fan;M. Poggi;S. Mattoccia
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Abstract

This paper proposes a lightweight yet effective network architecture for depth completion. It enables to fuse multi-modal and multi-level features through a Cascade Dense Connection Fusion Network. This is implemented by means of a dense connection fusion block, multi-scale features and a modality-aware aggregation mechanism. Our model is evaluated on the KITTI benchmark and achieves competitive results compared with state-of-the-art while counting much fewer parameters.
Proceedings of 33rd British Machine Vision Conference (BMVC 2022)
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R. Fan; Z. Li, M. Poggi, S. Mattoccia
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/902875
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