127 citations · 264 across the 15 of their papers we have counts for
15 papers
MonoNeRD: NeRF-like Representations for Monocular 3D Object Detection
Junkai Xu, Liang Peng, Haoran Cheng +5
In the field of monocular 3D detection, it is common practice to utilize scene geometric clues to enhance the detector's performance. However, many existing works adopt these clues…
Unsupervised Multiplex Graph Learning with Complementary and Consistent Information
Liang Peng, Xin Wang, Xiaofeng Zhu
Unsupervised multiplex graph learning (UMGL) has been shown to achieve significant effectiveness for different downstream tasks by exploring both complementary information and cons…
The RoboDepth Challenge: Methods and Advancements Towards Robust Depth Estimation
Lingdong Kong, Yaru Niu, Shaoyuan Xie +39
Accurate depth estimation under out-of-distribution (OoD) scenarios, such as adverse weather conditions, sensor failure, and noise contamination, is desirable for safety-critical a…
Envisioning a Next Generation Extended Reality Conferencing System with Efficient Photorealistic Human Rendering
Chuanyue Shen, Letian Zhang, Zhangsihao Yang +4
Meeting online is becoming the new normal. Creating an immersive experience for online meetings is a necessity towards more diverse and seamless environments. Efficient photorealis…
Learning Occupancy for Monocular 3D Object Detection
Liang Peng, Junkai Xu, Haoran Cheng +6
Monocular 3D detection is a challenging task due to the lack of accurate 3D information. Existing approaches typically rely on geometry constraints and dense depth estimates to fac…
Boosting Semi-Supervised 3D Object Detection with Semi-Sampling
Xiaopei Wu, Yang Zhao, Liang Peng +6
Current 3D object detection methods heavily rely on an enormous amount of annotations. Semi-supervised learning can be used to alleviate this issue. Previous semi-supervised 3D obj…
DID-M3D: Decoupling Instance Depth for Monocular 3D Object Detection
Liang Peng, Xiaopei Wu, Zheng Yang +2
Monocular 3D detection has drawn much attention from the community due to its low cost and setup simplicity. It takes an RGB image as input and predicts 3D boxes in the 3D space. T…
WeakM3D: Towards Weakly Supervised Monocular 3D Object Detection
Liang Peng, Senbo Yan, Boxi Wu +3
Monocular 3D object detection is one of the most challenging tasks in 3D scene understanding. Due to the ill-posed nature of monocular imagery, existing monocular 3D detection meth…
Sparse Fuse Dense: Towards High Quality 3D Detection with Depth Completion
Xiaopei Wu, Liang Peng, Honghui Yang +5
Current LiDAR-only 3D detection methods inevitably suffer from the sparsity of point clouds. Many multi-modal methods are proposed to alleviate this issue, while different represen…
GATE: Graph CCA for Temporal SElf-supervised Learning for Label-efficient fMRI Analysis
Liang Peng, Nan Wang, Jie Xu +2
In this work, we focus on the challenging task, neuro-disease classification, using functional magnetic resonance imaging (fMRI). In population graph-based disease analysis, graph…
FedNI: Federated Graph Learning with Network Inpainting for Population-Based Disease Prediction
Liang Peng, Nan Wang, Nicha Dvornek +2
Graph Convolutional Neural Networks (GCNs) are widely used for graph analysis. Specifically, in medical applications, GCNs can be used for disease prediction on a population graph,…
Multi-level Feature Learning for Contrastive Multi-view Clustering
Jie Xu, Huayi Tang, Yazhou Ren +3
Multi-view clustering can explore common semantics from multiple views and has attracted increasing attention. However, existing works punish multiple objectives in the same featur…
OCM3D: Object-Centric Monocular 3D Object Detection
Liang Peng, Fei Liu, Senbo Yan +2
Image-only and pseudo-LiDAR representations are commonly used for monocular 3D object detection. However, methods based on them have shortcomings of either not well capturing the s…
Lidar Point Cloud Guided Monocular 3D Object Detection
Liang Peng, Fei Liu, Zhengxu Yu +5
Monocular 3D object detection is a challenging task in the self-driving and computer vision community. As a common practice, most previous works use manually annotated 3D box label…
Geometry-based Occlusion-Aware Unsupervised Stereo Matching for Autonomous Driving
Liang Peng, Dan Deng, Deng Cai
Recently, there are emerging many stereo matching methods for autonomous driving based on unsupervised learning. Most of them take advantage of reconstruction losses to remove depe…