用掩码预测提升单目深度估计，无需额外推理开销
JEPADepth: Masked Predictive Representation Learning for Self-Supervised Monocular Depth Estimation
- 在预训练DINOv3编码器的表征空间中引入掩码预测损失
- 在KITTI上优于基线，零样本迁移在Make3D和Cityscapes表现顶尖
- 适合追求高精度且不想增加部署成本的深度估计研究者
自监督单目深度估计通常依赖于将深度、位姿和外观假设耦合的光度重建损失。本文提出JEPADepth，一种基于I-JEPA思想的自监督深度学习框架。该方法在标准光度管道基础上，加入由预训练DINOv3视觉变换器编码器产生的表征空间中的掩码预测损失。一个预测器从可见上下文区域的嵌入中推断目标区域嵌入，在推理时丢弃目标编码器与预测器，不增加部署开销。在KITTI数据集上，加入JEPA目标后性能持续优于相同DINOv3基线，且不改变推理架构。相较于以往自监督方法，JEPADepth在标准基准上达到与先进变压器方法相当的性能，并优于强效卷积神经网络基线。在零样本迁移（在KITTI上训练，无微调评估）下，其在Make3D和Cityscapes多个指标上达到最佳或接近最佳表现。
原文摘要 · Abstract (English)
Self-supervised monocular depth estimation typically relies on photometric reconstruction losses that couple depth, pose, and appearance assumptions. In this paper, we propose JEPADepth, a self-supervised monocular depth framework that incorporates a complementary training objective inspired by Image Joint-Embedding Predictive Architectures (I-JEPA) for self-supervised depth learning. Our method augments a standard photometric pipeline with a masked prediction loss computed in the representation space of a pretrained DINOv3 Vision Transformer encoder. A predictor infers target-region embeddings from visible context-region embeddings under structured masking, and is discarded along with the target encoder at inference time, adding no deployment cost. On KITTI, adding the JEPA objective consistently improves performance over the same DINOv3-based photometric baseline, without changing the inference-time architecture. Compared to prior monocular self-supervised methods, JEPADepth is competitive with state-of-the-art transformer-based approaches and outperforms strong CNN-based baselines on the standard benchmark. In zero-shot transfer (trained on KITTI and evaluated without fine-tuning), JEPADepth achieves the best or near-best performance among the compared methods on both Make3D and Cityscapes across multiple metrics.
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