A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Filters
Deep Predictive Neural Network: Unsupervised Learning for Hand Pose Estimation
2019
International Journal of Machine Learning and Computing
Preserved Fulltext
The discriminative approaches for hand pose estimation from depth images usually require dense annotated data to train a supervised network. ...
Index Terms-Deep learning, hand pose estimation, joint regression, predictive neural networks. Manuscript ...
poses and corresponding depth images for estimating 3D hand pose. ...
doi:10.18178/ijmlc.2019.9.4.822
fatcat:wiij7s7cmjegrivfs6tk2fy7da
End-to-end Global to Local CNN Learning for Hand Pose Recovery in Depth Data
[article]
2018
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Other Versions
Despite recent advances in 3D pose estimation of human hands, especially thanks to the advent of CNNs and depth cameras, this task is still far from being solved. ...
estimations, outperforming state-of-the-art results on NYU and SyntheticHand datasets. ...
We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan Xp GPU used for this research. ...
arXiv:1705.09606v2
fatcat:ntnfaa4pxfe2jnzjhts7rw7gpu
NeurAll: Towards a Unified Model for Visual Perception in Automated Driving
[article]
2019
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Note that this fulltext copy is not of the "primary" version of this work. The version it corresponds to is:
2019 pre-print arXiv:1902.03589v1 fatcat:pjq5q47o4zbhvgfd64fi6z2qme
Convolutional Neural Networks (CNNs) are successfully used for the important automotive visual perception tasks including object recognition, motion and depth estimation, visual SLAM, etc. ...
Indeed, the main bottleneck in automated driving systems is the limited processing power available on deployment hardware. ...
Depth regression decoder was constructed similar to FCN8 [33] decoder except the final layer was replaced with regression units instead of softmax to estimate depth. ...
arXiv:1902.03589v2
fatcat:ed6vnc2b7reqneiokvv4bd6hg4
Disentangling 3D Pose in A Dendritic CNN for Unconstrained 2D Face Alignment
[article]
2018
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL.
The file type is application/pdf.
Other Versions
Following a Bayesian formulation, we disentangle the 3D pose of a face image explicitly by conditioning the landmark estimation on pose, making it different from multi-tasking approaches. ...
Instead of increasing depth or width of the network, we train the CNN efficiently with Mask-Softmax Loss and hard sample mining to achieve upto 15% reduction in error compared to state-of-the-art methods ...
On the other hand, 3D pose is fairly stable to them and can be estimated directly from 2D image [31] . ...
arXiv:1802.06713v3
fatcat:tlmpaleaf5etzbtna2hvsafovu
Disentangling 3D Pose in a Dendritic CNN for Unconstrained 2D Face Alignment
2018
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Following a Bayesian formulation, we disentangle the 3D pose of a face image explicitly by conditioning the landmark estimation on pose, making it different from multi-tasking approaches. ...
Instead of increasing depth or width of the network, we train the CNN efficiently with Mask-Softmax Loss and hard sample mining to achieve upto 15% reduction in error compared to state-of-the-art methods ...
On the other hand, 3D pose is fairly stable to them and can be estimated directly from 2D image [30] . ...
doi:10.1109/cvpr.2018.00052
dblp:conf/cvpr/KumarC18
fatcat:qicy2r7emrclnhh2yl7zcbr5lq
3D Hand Pose Estimation using Simulation and Partial-Supervision with a Shared Latent Space
[article]
2018
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Tremendous amounts of expensive annotated data are a vital ingredient for state-of-the-art 3d hand pose estimation. ...
Accordingly, we form a shared latent space between three modalities: synthetic depth image, real depth image, and pose. ...
[28] propose a transductive regression forest that uses unlabeled and synthetic data to estimate the 3d hand pose. Shrivastava et al. ...
arXiv:1807.05380v1
fatcat:vpbv43uloba5lfabb67f2akezi
Foreground-aware Dense Depth Estimation for 360 Images
2020
Journal of WSCG
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
We further propose a novel auxiliary deep neural network to estimate both the depth of the omnidirectional images and the mask of the foreground objects, where the two tasks facilitate each other. ...
However, existing depth estimation approaches produce sub-optimal results on real-world omnidirectional images with dynamic foreground objects. ...
We then use augmented data to train depth estimation networks with the auxiliary MaskNet, and verified that the local depth loss can successfully improve the consistency of estimated depth within areas ...
doi:10.24132/jwscg.2020.28.10
fatcat:myjvc7kabrgivjs2ljrjms7p7q
BiHand: Recovering Hand Mesh with Multi-stage Bisected Hourglass Networks
[article]
2020
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
For quantitative evaluation, we conduct experiments on two public benchmarks, namely the Rendered Hand Dataset (RHD) and the Stereo Hand Pose Tracking Benchmark (STB). ...
3D hand estimation has been a long-standing research topic in computer vision. A recent trend aims not only to estimate the 3D hand joint locations but also to recover the mesh model. ...
Related Work Our method closely relates to 3D hand pose estimation and 3D hand mesh reconstruction problems.
3D Hand Pose Estimation Early works on 3D hand pose estimation mainly focused on regressing ...
arXiv:2008.05079v1
fatcat:z4hc3gyfq5a7vb35xdwil47cfa
DeepRM: Deep Recurrent Matching for 6D Pose Refinement
[article]
2022
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
Other Versions
The rendered images are then matched with the observed images to predict a rigid transform for updating the previous pose estimate. ...
To address this problem, we propose DeepRM, a novel recurrent network architecture for 6D pose refinement. DeepRM leverages initial coarse pose estimates to render synthetic images of target objects. ...
The proposed DeepRM method improves upon DeepIM [2] with several innovations, such as high resolution cropping, disentangled loss, variable renderer brightness, a scalable backbone based on EfficientNet ...
arXiv:2205.14474v2
fatcat:3rxvypntobh7rdpzzcbkwar2pe
Modelling Uncertainty in Deep Learning for Camera Relocalization
[article]
2016
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Note that this fulltext copy is not of the "primary" version of this work. The version it corresponds to is:
2015 pre-print arXiv:1509.05909v1 fatcat:jkxoedbbp5f4leigk3lw672mv4
We use a Bayesian convolutional neural network to regress the 6-DOF camera pose from a single RGB image. ...
Using a Bayesian convolutional neural network implementation we obtain an estimate of the model's relocalization uncertainty and improve state of the art localization accuracy on a large scale outdoor ...
At a shallower depth, with the first auxiliary pose regressor (green), the results are multi-modal. This is especially true for visually ambiguous images such as (c) in figure 2. ...
arXiv:1509.05909v2
fatcat:zarj7j42s5f2rehlkoikfjx6ve
Modelling uncertainty in deep learning for camera relocalization
2016
2016 IEEE International Conference on Robotics and Automation (ICRA)
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
We use a Bayesian convolutional neural network to regress the 6-DOF camera pose from a single RGB image. ...
Using a Bayesian convolutional neural network implementation we obtain an estimate of the model's relocalization uncertainty and improve state of the art localization accuracy on a large scale outdoor ...
At a shallower depth, with the first auxiliary pose regressor (green), the results are multi-modal. This is especially true for visually ambiguous images such as (c) in Figure 2 . ...
doi:10.1109/icra.2016.7487679
dblp:conf/icra/KendallC16
fatcat:7zfus43jt5efnpebfhiohmi4aq
Novel View Synthesis for Large-scale Scene using Adversarial Loss
[article]
2018
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL.
The file type is application/pdf.
Most of previous works focus on generating novel views of certain objects with a fixed background. ...
The inverse depth features are obtained from CNNs trained with sparse labeled depth values. This framework can easily fuse multiple images from different viewpoints. ...
Adversarial loss with a real input image classifier and the real pose θt regression. Adversarial loss with a generated image classifier and the fake random pose variables zp regression. ...
arXiv:1802.07064v1
fatcat:ecqfcuk7pbhmtg5lqq3f6gx2oe
Survey on depth and RGB image-based 3D hand shape and pose estimation
2021
Virtual Reality & Intelligent Hardware
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
hand shape and pose estimation. ...
With the availability of large-scale annotated hand datasets and the rapid developments of deep neural networks (DNNs), numerous DNN-based data-driven methods have been proposed for accurate and rapid ...
networks for 3D hand pose estimation. ...
doi:10.1016/j.vrih.2021.05.002
fatcat:4tbhftt3ira6fporaqlscqhsse
Learning Auxiliary Monocular Contexts Helps Monocular 3D Object Detection
[article]
2021
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
After training, the auxiliary context regression branches are discarded for better inference efficiency. ...
The key idea is that with the annotated 3D bounding boxes of objects in an image, there is a rich set of well-posed projected 2D supervision signals available in training, such as the projected corner ...
feature backbone and a list of regression heads with the same
One the one hand, without the auxiliary components, our module architecture for the essential parameters and the aux-
MonoCon is most ...
arXiv:2112.04628v1
fatcat:e5ev2xesvjgmpe5cfm57cscu6i
Aligning Latent Spaces for 3D Hand Pose Estimation
2019
2019 IEEE/CVF International Conference on Computer Vision (ICCV)
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Hand pose estimation from monocular RGB inputs is a highly challenging task. ...
In this work, we propose to learn a joint latent representation that leverages other modalities as weak labels to improve RGB-based hand pose estimation. ...
[11] use 3D voxels as input and regress the hand pose with a 3D CNN. ...
doi:10.1109/iccv.2019.00242
dblp:conf/iccv/YangLLY19
fatcat:tymbevymqraypfzhvii34awpsi
« Previous
Showing results 1 — 15 out of 4,937 results