LiRCDepth: Lightweight Radar-Camera Depth Estimation via Knowledge Distillation and Uncertainty Guidance

• URL: http://arxiv.org/abs/2412.16380v2
• Date: Fri, 27 Dec 2024 08:38:09 GMT
• Title: LiRCDepth: Lightweight Radar-Camera Depth Estimation via Knowledge Distillation and Uncertainty Guidance
• Authors: Huawei Sun, Nastassia Vysotskaya, Tobias Sukianto, Hao Feng, Julius Ott, Xiangyuan Peng, Lorenzo Servadei, Robert Wille,
• Abstract summary: LiRCDepth is a lightweight radar-camera depth estimation model.<n>We incorporate knowledge distillation to enhance the training process.<n>The model achieves a 6.6% improvement in MAE on the nuScenes dataset.
• Score: 5.9796425689255255
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recently, radar-camera fusion algorithms have gained significant attention as radar sensors provide geometric information that complements the limitations of cameras. However, most existing radar-camera depth estimation algorithms focus solely on improving performance, often neglecting computational efficiency. To address this gap, we propose LiRCDepth, a lightweight radar-camera depth estimation model. We incorporate knowledge distillation to enhance the training process, transferring critical information from a complex teacher model to our lightweight student model in three key domains. Firstly, low-level and high-level features are transferred by incorporating pixel-wise and pair-wise distillation. Additionally, we introduce an uncertainty-aware inter-depth distillation loss to refine intermediate depth maps during decoding. Leveraging our proposed knowledge distillation scheme, the lightweight model achieves a 6.6% improvement in MAE on the nuScenes dataset compared to the model trained without distillation. Code: https://github.com/harborsarah/LiRCDepth

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