ArticleSensors (Basel, Switzerland)2022
Lightweight Depth Completion Network with Local Similarity-Preserving Knowledge Distillation.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Geometric Feature Relationship-Based Knowledge Distillation for Ground Reaction Force Estimation.Measurement : journal of the International Measurement Confederation · 2026Article
- Selective Correlation Based Knowledge Distillation for Ground Reaction Force Estimation.Measurement : journal of the International Measurement Confederation · 2026Article
- GAC-Net: A Geometric-Attention Fusion Network for Sparse Depth Completion from LiDAR and Image.Sensors (Basel, Switzerland) · 2025Article
- Ground Reaction Force Estimation via Time-aware Knowledge Distillation.IEEE internet of things journal · 2025Article
- Simultaneous Color Registration and Depth Completion of Point Clouds with Curriculum Learning.Sensors (Basel, Switzerland) · 2025Article
- Unsupervised Depth Completion Guided by Visual Inertial System and Confidence.Sensors (Basel, Switzerland) · 2023Article
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Authors and funding
6 authors.
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Abstract
Depth perception capability is one of the essential requirements for various autonomous driving platforms. However, accurate depth estimation in a real-world setting is still a challenging problem due to high computational costs. In this paper, we propose a lightweight depth completion network for depth perception in real-world environments. To effectively transfer a teacher's knowledge, useful for the depth completion, we introduce local similarity-preserving knowledge distillation (LSPKD), which allows similarities between local neighbors to be transferred during the distillation. With our LSPKD, a lightweight student network is precisely guided by a heavy teacher network, regardless of the density of the ground-truth data. Experimental results demonstrate that our method is effective to reduce computational costs during both training and inference stages while achieving superior performance over other lightweight networks.
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