506 citations · 1.3k across the 52 of their papers we have counts for
11 papers · 1 filter
SST: Real-time End-to-end Monocular 3D Reconstruction via Sparse Spatial-Temporal Guidance
Chenyangguang Zhang, Zhiqiang Lou, Yan Di +2
Real-time monocular 3D reconstruction is a challenging problem that remains unsolved. Although recent end-to-end methods have demonstrated promising results, tiny structures and ge…
Learning to Annotate Part Segmentation with Gradient Matching
Yu Yang, Xiaotian Cheng, Hakan Bilen +1
The success of state-of-the-art deep neural networks heavily relies on the presence of large-scale labelled datasets, which are extremely expensive and time-consuming to annotate.…
Multi-Camera Collaborative Depth Prediction via Consistent Structure Estimation
Jialei Xu, Xianming Liu, Yuanchao Bai +4
Depth map estimation from images is an important task in robotic systems. Existing methods can be categorized into two groups including multi-view stereo and monocular depth estima…
Deep Lossy Plus Residual Coding for Lossless and Near-lossless Image Compression
Yuanchao Bai, Xianming Liu, Kai Wang +3
Lossless and near-lossless image compression is of paramount importance to professional users in many technical fields, such as medicine, remote sensing, precision engineering and…
CATRE: Iterative Point Clouds Alignment for Category-level Object Pose Refinement
Xingyu Liu, Gu Wang, Yi Li +1
While category-level 9DoF object pose estimation has emerged recently, previous correspondence-based or direct regression methods are both limited in accuracy due to the huge intra…
PUERT: Probabilistic Under-sampling and Explicable Reconstruction Network for CS-MRI
Jingfen Xie, Jian Zhang, Yongbing Zhang +1
Compressed Sensing MRI (CS-MRI) aims at reconstructing de-aliased images from sub-Nyquist sampling k-space data to accelerate MR Imaging, thus presenting two basic issues, i.e., wh…
Self-Supervised Arbitrary-Scale Point Clouds Upsampling via Implicit Neural Representation
Wenbo Zhao, Xianming Liu, Zhiwei Zhong +4
Point clouds upsampling is a challenging issue to generate dense and uniform point clouds from the given sparse input. Most existing methods either take the end-to-end supervised l…
Shadows can be Dangerous: Stealthy and Effective Physical-world Adversarial Attack by Natural Phenomenon
Yiqi Zhong, Xianming Liu, Deming Zhai +2
Estimating the risk level of adversarial examples is essential for safely deploying machine learning models in the real world. One popular approach for physical-world attacks is to…
Occlusion-Aware Self-Supervised Monocular 6D Object Pose Estimation
Gu Wang, Fabian Manhardt, Xingyu Liu +2
6D object pose estimation is a fundamental yet challenging problem in computer vision. Convolutional Neural Networks (CNNs) have recently proven to be capable of predicting reliabl…
GPV-Pose: Category-level Object Pose Estimation via Geometry-guided Point-wise Voting
Yan Di, Ruida Zhang, Zhiqiang Lou +4
While 6D object pose estimation has recently made a huge leap forward, most methods can still only handle a single or a handful of different objects, which limits their application…
Which Style Makes Me Attractive? Interpretable Control Discovery and Counterfactual Explanation on StyleGAN
Bo Li, Qiulin Wang, Jiquan Pei +2
The semantically disentangled latent subspace in GAN provides rich interpretable controls in image generation. This paper includes two contributions on semantic latent subspace ana…