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Found 17 papers, 5 papers with code
Video Depth Anything: Consistent Depth Estimation for Super-Long Videos
The model is trained on a joint dataset of video depth and unlabeled images, similar to Depth Anything V2.
MonoPlane: Exploiting Monocular Geometric Cues for Generalizable 3D Plane Reconstruction
This paper presents a generalizable 3D plane detection and reconstruction framework named MonoPlane.
Lazy Visual Localization via Motion Averaging
Visual (re)localization is critical for various applications in computer vision and robotics.
ParticleSfM: Exploiting Dense Point Trajectories for Localizing Moving Cameras in the Wild
In addition, our method is able to retain reasonable accuracy of camera poses on fully static scenes, which consistently outperforms strong state-of-the-art dense correspondence based methods with end-to-end deep learning, demonstrating the potential of dense indirect methods based on optical flow and point trajectories.
A Confidence-based Iterative Solver of Depths and Surface Normals for Deep Multi-view Stereo
Experimental results on ScanNet and RGB-D Scenes V2 demonstrate state-of-the-art performance of the proposed deep MVS system on multi-view depth estimation, with our proposed solver consistently improving the depth quality over both conventional and deep learning based MVS pipelines.
GPO: Global Plane Optimization for Fast and Accurate Monocular SLAM Initialization
Initialization is essential to monocular Simultaneous Localization and Mapping (SLAM) problems.
Towards Good Practices for Video Object Segmentation
Semi-supervised video object segmentation is an interesting yet challenging task in machine learning.
An Empirical Study of Propagation-based Methods for Video Object Segmentation
While propagation-based approaches have achieved state-of-the-art performance for video object segmentation, the literature lacks a fair comparison of different methods using the same settings.
Motion feature augmented network for dynamic hand gesture recognition from skeletal data
Dynamic hand gesture recognition has attracted increasing attention because of its importance for human–computer interaction.
Multi-Domain Pose Network for Multi-Person Pose Estimation and Tracking
Multi-person human pose estimation and tracking in the wild is important and challenging.
Multi-Person Pose Estimation
Multi-Person Pose Estimation and Tracking
+1
Two-Stream Binocular Network: Accurate Near Field Finger Detection Based On Binocular Images
Different from previous works, we propose a new framework, named Two-Stream Binocular Network (TSBnet) to detect fingertips from binocular images directly.
Interactive Hand Pose Estimation: Boosting accuracy in localizing extended finger joints
In the reality of HMI, joints in fingers stretching out, especially corresponding fingertips, are much more important than other joints.
Pose Guided Structured Region Ensemble Network for Cascaded Hand Pose Estimation
The proposed method extracts regions from the feature maps of convolutional neural network under the guide of an initially estimated pose, generating more optimal and representative features for hand pose estimation.
Ranked #8 on
Hand Pose Estimation
on HANDS 2017
Motion Feature Augmented Recurrent Neural Network for Skeleton-based Dynamic Hand Gesture Recognition
Dynamic hand gesture recognition has attracted increasing interests because of its importance for human computer interaction.
Ranked #9 on
Hand Gesture Recognition
on DHG-28
Towards Good Practices for Deep 3D Hand Pose Estimation
3D hand pose estimation from single depth image is an important and challenging problem for human-computer interaction.
Ranked #4 on
Pose Estimation
on ITOP top-view
Region Ensemble Network: Improving Convolutional Network for Hand Pose Estimation
Hand pose estimation from monocular depth images is an important and challenging problem for human-computer interaction.
Ranked #11 on
Hand Pose Estimation
on MSRA Hands
Two-stream convolutional neural network for accurate RGB-D fingertip detection using depth and edge information
Accurate detection of fingertips in depth image is critical for human-computer interaction.
