36 citations · 47 across the 11 of their papers we have counts for
13 papers
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…
An Instance-Centric Panoptic Occupancy Prediction Benchmark for Autonomous Driving
Yi Feng, Junwu E, Zizhan Guo +3
Panoptic occupancy prediction aims to jointly infer voxel-wise semantics and instance identities within a unified 3D scene representation. Nevertheless, progress in this field rema…
Rebenchmarking Unsupervised Monocular 3D Occupancy Prediction
Zizhan Guo, Yi Feng, Mengtan Zhang +3
Inferring the 3D structure from a single image, particularly in occluded regions, remains a fundamental yet unsolved challenge in vision-centric autonomous driving. Existing unsupe…
Discriminately Treating Motion Components Evolves Joint Depth and Ego-Motion Learning
Mengtan Zhang, Zizhan Guo, Hongbo Zhao +6
Unsupervised learning of depth and ego-motion, two fundamental 3D perception tasks, has made significant strides in recent years. However, most methods treat ego-motion as an auxil…
ViPOcc: Leveraging Visual Priors from Vision Foundation Models for Single-View 3D Occupancy Prediction
Yi Feng, Yu Han, Xijing Zhang +3
Inferring the 3D structure of a scene from a single image is an ill-posed and challenging problem in the field of vision-centric autonomous driving. Existing methods usually employ…
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…
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…
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…
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…
Three-Filters-to-Normal+: Revisiting Discontinuity Discrimination in Depth-to-Normal Translation
Jingwei Yang, Bohuan Xue, Yi Feng +3
This article introduces three-filters-to-normal+ (3F2N+), an extension of our previous work three-filters-to-normal (3F2N), with a specific focus on incorporating discontinuity dis…
Freespace Optical Flow Modeling for Automated Driving
Yi Feng, Ruge Zhang, Jiayuan Du +2
Optical flow and disparity are two informative visual features for autonomous driving perception. They have been used for a variety of applications, such as obstacle and lane detec…
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…