将深度补全转化为增强，用图像信息自适应修复稀疏深度图
Completion as Enhancement: A Degradation-Aware Selective Image Guided Network for Depth Completion
- 先用非CNN方法粗略填充稀疏深度，消除不规则采样带来的歧义
- 通过自监督退化桥，选择图像高频特征（如边缘）补偿深度细节
- 结合Mamba结构动态融合多模态信息，适合高精度深度重建任务
本文提出选择性图像引导网络（SigNet），首次将深度补全问题转化为深度增强。不同于直接使用卷积神经网络处理不规则稀疏数据，SigNet首先通过非CNN的稠密化工具对稀疏深度进行粗略填充，得到稠密但粗糙的深度图，从而避免了传统方法中因不规则采样导致的匹配与歧义问题。随后，SigNet重新定义补全为增强，建立粗深度与目标稠密深度之间的自监督退化桥梁，实现有效的RGB-D融合。为此，SigNet利用隐式退化机制，自适应地选择RGB图像中的高频成分（如边缘）来弥补粗深度的缺失细节。该退化机制进一步融入多模态条件化的Mamba结构，动态生成状态参数，实现高效全局高频信息交互。在NYUv2、DIML、SUN RGBD和TOFDC四个数据集上进行了大量实验，结果表明SigNet达到了当前最优性能。
原文摘要 · Abstract (English)
In this paper, we introduce the Selective Image Guided Network (SigNet), a novel degradation-aware framework that transforms depth completion into depth enhancement for the first time. Moving beyond direct completion using convolutional neural networks (CNNs), SigNet initially densifies sparse depth data through non-CNN densification tools to obtain coarse yet dense depth. This approach eliminates the mismatch and ambiguity caused by direct convolution over irregularly sampled sparse data. Subsequently, SigNet redefines completion as enhancement, establishing a self-supervised degradation bridge between the coarse depth and the targeted dense depth for effective RGB-D fusion. To achieve this, SigNet leverages the implicit degradation to adaptively select high-frequency components (e.g., edges) of RGB data to compensate for the coarse depth. This degradation is further integrated into a multi-modal conditional Mamba, dynamically generating the state parameters to enable efficient global high-frequency information interaction. We conduct extensive experiments on the NYUv2, DIML, SUN RGBD, and TOFDC datasets, demonstrating the state-of-the-art (SOTA) performance of SigNet.
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。