5 papers
SAP-Depth: Scene-Adaptive Probability Framework for Self-Supervised Monocular Depth Estimation
Bowen He, Li Zhang, Yujie Chen +1
Self-supervised monocular depth estimation remains challenging due to the loss of fine-grained details and limited adaptability to varying scene structures. In this paper, we propo…
Towards Robust Driving Perception: A Flexible Scale-Driven Family for Self-Supervised Monocular Depth Estimation
Zhaowen Zhu, Li Zhang, Yujie Chen +3
Self-Supervised Monocular Depth Estimation (MDE) has garnered attention in recent years due to its independence from ground truth. However, most existing models are limited to a si…
3D Consistency Optimization for Self-Supervised Monocular Video Depth Estimation
Yuanye Liu, Ke Zhang, Junzhe Jiang +3
Reliable monocular video depth estimation is crucial for downstream 3D reasoning and embodied AI in endoscopic navigation. However, existing self-supervised approaches typically tr…
PuriLight: A Lightweight Shuffle and Purification Framework for Monocular Depth Estimation
Yujie Chen, Li Zhang, Xiaomeng Chu +1
We propose PuriLight, a lightweight and efficient framework for self-supervised monocular depth estimation, to address the dual challenges of computational efficiency and detail pr…
VistaDepth: Improving far-range Depth Estimation with Spectral Modulation and Adaptive Reweighting
Mingxia Zhan, Li Zhang, Yingjie Wang +3
Monocular depth estimation (MDE) aims to infer per-pixel depth from a single RGB image. While diffusion models have advanced MDE with impressive generalization, they often exhibit…