通过学习并去除鸟瞰图特征中的固有噪声，提升语义分割精度。
BEV-Denoise: Learning Intrinsic Noise for Accurate Bird's-Eye-View Semantic Segmentation
- 用UNet结构估计鸟瞰图特征的内在噪声
- 在nuScenes数据集上显著提升分割准确率
- 适用于各类视角转换模型，尤其适合自动驾驶场景
本文提出一种名为BEV-Denoise的框架，通过估计并移除鸟瞰图（BEV）特征中的固有噪声，实现高精度的鸟瞰图语义分割。受去噪扩散概率模型（DDPM）噪声估计能力的启发，我们设计了一个基于UNet的噪声估计模块，可学习从已生成的BEV特征中估计噪声。估计出的噪声被减去后，再输入到BEV地图解码器中进行最终预测。为辅助噪声估计模块的训练，我们采用任务分解（TD）的渐进式学习范式，利用预训练的BEV地图自编码器训练视图变换（VT）编码器。通过大量实验，我们总结出三个关键洞察，对性能提升至关重要。本框架被应用于四种现有模型，涵盖三种主流的视角转换范式。在大规模真实世界数据集nuScenes上的实验结果验证了该框架的有效性。
原文摘要 · Abstract (English)
In this paper, we present a framework dubbed \textbf{BEV-Denoise} that estimates and removes intrinsic noise from learned Bird's-Eye-View (BEV) features to achieve accurate BEV semantic segmentation. Inspired by the noise estimation capability of Denoising Diffusion Probabilistic Models (DDPM), we design a UNet-based noise estimation module that learns to estimate the noise from the learned BEV features. The estimated noise is then subtracted from the BEV features and fed to BEV map decoders for the final prediction results. To facilitate supervision for the noise estimation module, we follow a sequential learning paradigm called Task Decomposition (TD) where a pre-trained BEV map autoencoder is employed to train a view transformation (VT) encoder. We share three key insights learned from our intensive experiments that are critical for improved performance. We apply our framework to four existing models, encompassing the three major VT paradigms. Experimental results on a large-scale real-world dataset, nuScenes, demonstrate the effectiveness of our framework.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。