QD-BEV : Quantization-aware View-guided Distillation for Multi-view 3D Object Detection
This work addresses efficiency challenges for real-time perception in autonomous driving, though it is incremental as it builds on existing BEV detection methods.
The paper tackles the problem of high memory consumption and latency in multi-view 3D object detection models for vehicle deployment by proposing QD-BEV, a quantization-aware method with view-guided distillation, achieving up to 8x model compression and performance gains, such as 37.2% NDS with 15.8 MB size on nuScenes.
Multi-view 3D detection based on BEV (bird-eye-view) has recently achieved significant improvements. However, the huge memory consumption of state-of-the-art models makes it hard to deploy them on vehicles, and the non-trivial latency will affect the real-time perception of streaming applications. Despite the wide application of quantization to lighten models, we show in our paper that directly applying quantization in BEV tasks will 1) make the training unstable, and 2) lead to intolerable performance degradation. To solve these issues, our method QD-BEV enables a novel view-guided distillation (VGD) objective, which can stabilize the quantization-aware training (QAT) while enhancing the model performance by leveraging both image features and BEV features. Our experiments show that QD-BEV achieves similar or even better accuracy than previous methods with significant efficiency gains. On the nuScenes datasets, the 4-bit weight and 6-bit activation quantized QD-BEV-Tiny model achieves 37.2% NDS with only 15.8 MB model size, outperforming BevFormer-Tiny by 1.8% with an 8x model compression. On the Small and Base variants, QD-BEV models also perform superbly and achieve 47.9% NDS (28.2 MB) and 50.9% NDS (32.9 MB), respectively.