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Found 15 papers, 3 papers with code
Improving Real-Time Omnidirectional 3D Multi-Person Human Pose Estimation with People Matching and Unsupervised 2D-3D Lifting
Therefore, this paper presents one of the first 3D multi-person human pose estimation systems that is able to work in real-time and is also able to handle basic forms of occlusion.
Scalable Multi-modal Model Predictive Control via Duality-based Interaction Predictions
We propose a hierarchical architecture designed for scalable real-time Model Predictive Control (MPC) in complex, multi-modal traffic scenarios.
Depth Insight -- Contribution of Different Features to Indoor Single-image Depth Estimation
To this end, in this work, we quantify the relative contributions of the known cues of depth in a monocular depth estimation setting using an indoor scene data set.
Unsupervised Multi-Person 3D Human Pose Estimation From 2D Poses Alone
To address the issue of perspective ambiguity, we expand upon prior work by predicting the cameras' elevation angle relative to the subjects' pelvis.
LInKs "Lifting Independent Keypoints" -- Partial Pose Lifting for Occlusion Handling with Improved Accuracy in 2D-3D Human Pose Estimation
Furthermore, our method excels in accurately retrieving complete 3D poses even in the presence of occlusions, making it highly applicable in situations where complete 2D pose information is unavailable.
MatSpectNet: Material Segmentation Network with Domain-Aware and Physically-Constrained Hyperspectral Reconstruction
The network leverages the principles of colour perception in modern cameras to constrain the reconstructed hyperspectral images and employs the domain adaptation method to generalise the hyperspectral reconstruction capability from a spectral recovery dataset to material segmentation datasets.
DBAT: Dynamic Backward Attention Transformer for Material Segmentation with Cross-Resolution Patches
By analysing the cross-resolution features and the attention weights, this paper interprets how the DBAT learns from image patches.
Optimising 2D Pose Representation: Improve Accuracy, Stability and Generalisability Within Unsupervised 2D-3D Human Pose Estimation
With a maximum architecture capacity of 6 residual blocks, we evaluate the performance of 5 models which each represent a 2D pose differently during the adversarial unsupervised 2D-3D HPE process.
"Teaching Independent Parts Separately" (TIPSy-GAN) : Improving Accuracy and Stability in Unsupervised Adversarial 2D to 3D Pose Estimation
We show that for two independent generators training adversarially has improved stability than that of a solo generator which collapses.
Ranked #46 on
3D Human Pose Estimation
on MPI-INF-3DHP
(AUC metric)
Don't read, just look: Main content extraction from web pages using visual features
We propose this first browsing area, which is the top side of a web document for simulating situations where a user first encountered a web page.
Can Super Resolution be used to improve Human Pose Estimation in Low Resolution Scenarios?
Second, the keypoint detection performance gained is dependent on that persons pixel count in the original image prior to any application of SR; keypoint detection performance was improved when SR was applied to people with a small initial segmentation area, but degrades as this becomes larger.
EdgeNet: Semantic Scene Completion from a Single RGB-D Image
Semantic scene completion is the task of predicting a complete 3D representation of volumetric occupancy with corresponding semantic labels for a scene from a single point of view.
Ranked #22 on
3D Semantic Scene Completion
on NYUv2
Temporally Coherent General Dynamic Scene Reconstruction
Existing techniques for dynamic scene reconstruction from multiple wide-baseline cameras primarily focus on reconstruction in controlled environments, with fixed calibrated cameras and strong prior constraints.
Temporally coherent 4D reconstruction of complex dynamic scenes
Sparse-to-dense temporal correspondence is integrated with joint multi-view segmentation and reconstruction to obtain a complete 4D representation of static and dynamic objects.
General Dynamic Scene Reconstruction from Multiple View Video
The primary contributions of this paper are twofold: an automatic method for initial coarse dynamic scene segmentation and reconstruction without prior knowledge of background appearance or structure; and a general robust approach for joint segmentation refinement and dense reconstruction of dynamic scenes from multiple wide-baseline static or moving cameras.
