63 citations · 78 across the 13 of their papers we have counts for
11 papers · 1 filter
The Midas Touch for Metric Depth: Interpretable Metricization for Generalizable Depth Perception
Mengtan Zhang, Yu Ma, Shiqi Jiang +6
Recent foundation models have drastically improved the generalization of monocular relative depth estimation, yet recovering accurate metric depth from their estimates for depth pe…
Fully Exploiting Vision Foundation Model's Profound Prior Knowledge for Generalizable RGB-Depth Driving Scene Parsing
Sicen Guo, Tianyou Wen, Chuang-Wei Liu +2
Recent vision foundation models (VFMs), typically based on Vision Transformer (ViT), have significantly advanced numerous computer vision tasks. Despite their success in tasks focu…
These Maps Are Made by Propagation: Adapting Deep Stereo Networks to Road Scenarios with Decisive Disparity Diffusion
Chuang-Wei Liu, Yikang Zhang, Qijun Chen +2
Stereo matching has emerged as a cost-effective solution for road surface 3D reconstruction, garnering significant attention towards improving both computational efficiency and acc…
RoadFormer+: Delivering RGB-X Scene Parsing through Scale-Aware Information Decoupling and Advanced Heterogeneous Feature Fusion
Jianxin Huang, Jiahang Li, Ning Jia +4
Task-specific data-fusion networks have marked considerable achievements in urban scene parsing. Among these networks, our recently proposed RoadFormer successfully extracts hetero…
SCIPaD: Incorporating Spatial Clues into Unsupervised Pose-Depth Joint Learning
Yi Feng, Zizhan Guo, Qijun Chen +1
Unsupervised monocular depth estimation frameworks have shown promising performance in autonomous driving. However, existing solutions primarily rely on a simple convolutional neur…
HAPNet: Toward Superior RGB-Thermal Scene Parsing via Hybrid, Asymmetric, and Progressive Heterogeneous Feature Fusion
Jiahang Li, Peng Yun, Yang Xu +5
Data-fusion networks have shown significant promise for RGB-thermal scene parsing. However, the majority of existing studies have relied on symmetric duplex encoders for heterogene…
SNE-RoadSegV2: Advancing Heterogeneous Feature Fusion and Fallibility Awareness for Freespace Detection
Yi Feng, Yu Ma, Qijun Chen +2
Feature-fusion networks with duplex encoders have proven to be an effective technique to solve the freespace detection problem. However, despite the compelling results achieved by…
RoadFormer: Duplex Transformer for RGB-Normal Semantic Road Scene Parsing
Jiahang Li, Yikang Zhang, Peng Yun +3
The recent advancements in deep convolutional neural networks have shown significant promise in the domain of road scene parsing. Nevertheless, the existing works focus primarily o…
D2NT: A High-Performing Depth-to-Normal Translator
Yi Feng, Bohuan Xue, Ming Liu +2
Surface normal holds significant importance in visual environmental perception, serving as a source of rich geometric information. However, the state-of-the-art (SoTA) surface norm…
UDTIRI: An Online Open-Source Intelligent Road Inspection Benchmark Suite
Sicen Guo, Jiahang Li, Yi Feng +7
In the nascent domain of urban digital twins (UDT), the prospects for leveraging cutting-edge deep learning techniques are vast and compelling. Particularly within the specialized…
SIM2E: Benchmarking the Group Equivariant Capability of Correspondence Matching Algorithms
Shuai Su, Zhongkai Zhao, Yixin Fei +3
Correspondence matching is a fundamental problem in computer vision and robotics applications. Solving correspondence matching problems using neural networks has been on the rise r…