360 citations · 567 across the 25 of their papers we have counts for
25 papers
TIDE: Temporally Incremental Disparity Estimation via Pattern Flow in Structured Light System
Rukun Qiao, Hiroshi Kawasaki, Hongbin Zha
We introduced Temporally Incremental Disparity Estimation Network (TIDE-Net), a learning-based technique for disparity computation in mono-camera structured light systems. In our h…
Active Neural Mapping
Zike Yan, Haoxiang Yang, Hongbin Zha
We address the problem of active mapping with a continually-learned neural scene representation, namely Active Neural Mapping. The key lies in actively finding the target space to…
ECT: Fine-grained Edge Detection with Learned Cause Tokens
Shaocong Xu, Xiaoxue Chen, Yuhang Zheng +4
In this study, we tackle the challenging fine-grained edge detection task, which refers to predicting specific edges caused by reflectance, illumination, normal, and depth changes,…
From Semi-supervised to Omni-supervised Room Layout Estimation Using Point Clouds
Huan-ang Gao, Beiwen Tian, Pengfei Li +5
Room layout estimation is a long-existing robotic vision task that benefits both environment sensing and motion planning. However, layout estimation using point clouds (PCs) still…
SC-wLS: Towards Interpretable Feed-forward Camera Re-localization
Xin Wu, Hao Zhao, Shunkai Li +2
Visual re-localization aims to recover camera poses in a known environment, which is vital for applications like robotics or augmented reality. Feed-forward absolute camera pose re…
Transferable End-to-end Room Layout Estimation via Implicit Encoding
Hao Zhao, Rene Ranftl, Yurong Chen +1
We study the problem of estimating room layouts from a single panorama image. Most former works have two stages: feature extraction and parametric model fitting. Here we propose an…
Continual Neural Mapping: Learning An Implicit Scene Representation from Sequential Observations
Zike Yan, Yuxin Tian, Xuesong Shi +3
Recent advances have enabled a single neural network to serve as an implicit scene representation, establishing the mapping function between spatial coordinates and scene propertie…
Online Learning of a Probabilistic and Adaptive Scene Representation
Zike Yan, Xin Wang, Hongbin Zha
Constructing and maintaining a consistent scene model on-the-fly is the core task for online spatial perception, interpretation, and action. In this paper, we represent the scene w…
Generalizing to the Open World: Deep Visual Odometry with Online Adaptation
Shunkai Li, Xin Wu, Yingdian Cao +1
Despite learning-based visual odometry (VO) has shown impressive results in recent years, the pretrained networks may easily collapse in unseen environments. The large domain gap b…
Line Flow based SLAM
Qiuyuan Wang, Zike Yan, Junqiu Wang +3
We propose a visual SLAM method by predicting and updating line flows that represent sequential 2D projections of 3D line segments. While feature-based SLAM methods have achieved e…
Deep Visual Odometry with Adaptive Memory
Fei Xue, Xin Wang, Junqiu Wang +1
We propose a novel deep visual odometry (VO) method that considers global information by selecting memory and refining poses. Existing learning-based methods take the VO task as a…
Self-Supervised Deep Visual Odometry with Online Adaptation
Shunkai Li, Xin Wang, Yingdian Cao +3
Self-supervised VO methods have shown great success in jointly estimating camera pose and depth from videos. However, like most data-driven methods, existing VO networks suffer fro…
Sequential Adversarial Learning for Self-Supervised Deep Visual Odometry
Shunkai Li, Fei Xue, Xin Wang +2
We propose a self-supervised learning framework for visual odometry (VO) that incorporates correlation of consecutive frames and takes advantage of adversarial learning. Previous m…
Local Supports Global: Deep Camera Relocalization with Sequence Enhancement
Fei Xue, Xin Wang, Zike Yan +3
We propose to leverage the local information in image sequences to support global camera relocalization. In contrast to previous methods that regress global poses from single image…
Beyond Tracking: Selecting Memory and Refining Poses for Deep Visual Odometry
Fei Xue, Xin Wang, Shunkai Li +3
Most previous learning-based visual odometry (VO) methods take VO as a pure tracking problem. In contrast, we present a VO framework by incorporating two additional components call…
Deep Comprehensive Correlation Mining for Image Clustering
Jianlong Wu, Keyu Long, Fei Wang +4
Recent developed deep unsupervised methods allow us to jointly learn representation and cluster unlabelled data. These deep clustering methods mainly focus on the correlation among…
Guided Feature Selection for Deep Visual Odometry
Fei Xue, Qiuyuan Wang, Xin Wang +3
We present a novel end-to-end visual odometry architecture with guided feature selection based on deep convolutional recurrent neural networks. Different from current monocular vis…
PSDF Fusion: Probabilistic Signed Distance Function for On-the-fly 3D Data Fusion and Scene Reconstruction
Wei Dong, Qiuyuan Wang, Xin Wang +1
We propose a novel 3D spatial representation for data fusion and scene reconstruction. Probabilistic Signed Distance Function (Probabilistic SDF, PSDF) is proposed to depict uncert…
Recurrent Squeeze-and-Excitation Context Aggregation Net for Single Image Deraining
Xia Li, Jianlong Wu, Zhouchen Lin +2
Rain streaks can severely degrade the visibility, which causes many current computer vision algorithms fail to work. So it is necessary to remove the rain from images. We propose a…
Essential Tensor Learning for Multi-view Spectral Clustering
Jianlong Wu, Zhouchen Lin, Hongbin Zha
Multi-view clustering attracts much attention recently, which aims to take advantage of multi-view information to improve the performance of clustering. However, most recent work m…
An Efficient Volumetric Mesh Representation for Real-time Scene Reconstruction using Spatial Hashing
Wei Dong, Jieqi Shi, Weijie Tang +2
Mesh plays an indispensable role in dense real-time reconstruction essential in robotics. Efforts have been made to maintain flexible data structures for 3D data fusion, yet an eff…
Alternating Multi-bit Quantization for Recurrent Neural Networks
Chen Xu, Jianqiang Yao, Zhouchen Lin +4
Recurrent neural networks have achieved excellent performance in many applications. However, on portable devices with limited resources, the models are often too large to deploy. F…
A Unified Convex Surrogate for the Schatten- Norm
Chen Xu, Zhouchen Lin, Hongbin Zha
The Schatten- norm () has been widely used to replace the nuclear norm for better approximating the rank function. However, existing methods are either 1) not scalable fo…
Relaxed Majorization-Minimization for Non-smooth and Non-convex Optimization
Chen Xu, Zhouchen Lin, Zhenyu Zhao +1
We propose a new majorization-minimization (MM) method for non-smooth and non-convex programs, which is general enough to include the existing MM methods. Besides the local majoriz…