夜间占位预测新方法，通过光照感知表示提升暗光环境下的识别精度。
See through the Dark: Learning Illumination-affined Representations for Nighttime Occupancy Prediction
- 基于光照先验的自适应增强，区分真实黑暗与伪暗区域。
- 2D-IGS与3D-IDP协同缓解局部过曝和欠曝导致的特征退化。
- 适用于自动驾驶、机器人夜间场景的占位理解，尤其适合弱光环境。
占位预测旨在估计三维空间中被占据区域的分布及其语义标签。现有视觉方法在白天数据集上表现良好，但在夜间因可视性差和光照挑战而性能下降。为此，本文提出LIAR框架，学习光照感知表征。LIAR首先引入选择性低光图像增强（SLLIE），利用白天场景的光照先验，自适应判断夜间图像是否真正黑暗或已足够明亮，实现更精准的全局增强。基于SLLIE生成的光照图，进一步设计两个光照感知模块：2D光照引导采样（2D-IGS）和3D光照驱动投影（3D-IDP），分别应对局部欠曝和过曝问题。其中，2D-IGS根据光照图调节特征采样位置，在暗区增加偏移量，明区减小偏移量，缓解欠曝区域特征退化；3D-IDP则构建光照强度场，向BEV上下文精炼过程提供优化的残差查询，增强过曝区域语义理解。在真实与合成数据集上的大量实验表明，LIAR在复杂夜间场景下表现优异。源代码与预训练模型已公开。
原文摘要 · Abstract (English)
Occupancy prediction aims to estimate the 3D spatial distribution of occupied regions along with their corresponding semantic labels. Existing vision-based methods perform well on daytime benchmarks but struggle in nighttime scenarios due to limited visibility and challenging lighting conditions. To address these challenges, we propose LIAR, a novel framework that learns illumination-affined representations. LIAR first introduces Selective Low-light Image Enhancement (SLLIE), which leverages the illumination priors from daytime scenes to adaptively determine whether a nighttime image is genuinely dark or sufficiently well-lit, enabling more targeted global enhancement. Building on the illumination maps generated by SLLIE, LIAR further incorporates two illumination-aware components: 2D Illumination-guided Sampling (2D-IGS) and 3D Illumination-driven Projection (3D-IDP), to respectively tackle local underexposure and overexposure. Specifically, 2D-IGS modulates feature sampling positions according to illumination maps, assigning larger offsets to darker regions and smaller ones to brighter regions, thereby alleviating feature degradation in underexposed areas. Subsequently,3D-IDP enhances semantic understanding in overexposed regions by constructing illumination intensity fields and supplying refined residual queries to the BEV context refinement process. Extensive experiments on both real and synthetic datasets demonstrate the superior performance of LIAR under challenging nighttime scenarios. The source code and pretrained models are available [here](https://github.com/yanzq95/LIAR).
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。