36 citations · 41 across the 18 of their papers we have counts for
11 papers · 1 filter
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…
TiCoSS: Tightening the Coupling between Semantic Segmentation and Stereo Matching within A Joint Learning Framework
Guanfeng Tang, Zhiyuan Wu, Jiahang Li +5
Semantic segmentation and stereo matching, respectively analogous to the ventral and dorsal streams in our human brain, are two key components of autonomous driving perception syst…
DCPI-Depth: Explicitly Infusing Dense Correspondence Prior to Unsupervised Monocular Depth Estimation
Mengtan Zhang, Yi Feng, Qijun Chen +1
There has been a recent surge of interest in learning to perceive depth from monocular videos in an unsupervised fashion. A key challenge in this field is achieving robust and accu…
Playing to Vision Foundation Model's Strengths in Stereo Matching
Chuang-Wei Liu, Qijun Chen, Rui Fan
Stereo matching has become a key technique for 3D environment perception in intelligent vehicles. For a considerable time, convolutional neural networks (CNNs) have remained the ma…
Online,Target-Free LiDAR-Camera Extrinsic Calibration via Cross-Modal Mask Matching
Zhiwei Huang, Yikang Zhang, Qijun Chen +1
LiDAR-camera extrinsic calibration (LCEC) is crucial for data fusion in intelligent vehicles. Offline, target-based approaches have long been the preferred choice in this field. Ho…
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…
LIX: Implicitly Infusing Spatial Geometric Prior Knowledge into Visual Semantic Segmentation for Autonomous Driving
Sicen Guo, Ziwei Long, Zhiyuan Wu +3
Despite the impressive performance achieved by data-fusion networks with duplex encoders for visual semantic segmentation, they become ineffective when spatial geometric data are n…
SM-Net: Joint Learning of Semantic Segmentation and Stereo Matching for Autonomous Driving
Zhiyuan Wu, Yi Feng, Chuang-Wei Liu +3
Semantic segmentation and stereo matching are two essential components of 3D environmental perception systems for autonomous driving. Nevertheless, conventional approaches often ad…