Search Results for author:
Found 8 papers, 6 papers with code
Self-supervised learning of object pose estimation using keypoint prediction
We designed a network to generate a proxy ground-truth heatmap from a set of keypoints distributed all over the category-specific mean shape, where each is represented by a unique color on a labeled texture.
Registration Loss Learning for Deep Probabilistic Point Set Registration
This is possible as the probabilistic registration is fully differentiable, and the result is a learning framework that is truly end-to-end.
Learning What to Learn for Video Object Segmentation
This allows us to achieve a rich internal representation of the target in the current frame, significantly increasing the segmentation accuracy of our approach.
Learning Fast and Robust Target Models for Video Object Segmentation
The target appearance model consists of a light-weight module, which is learned during the inference stage using fast optimization techniques to predict a coarse but robust target segmentation.
Discriminative Online Learning for Fast Video Object Segmentation
We propose a novel approach, based on a dedicated target appearance model that is exclusively learned online to discriminate between the target and background image regions.
Density Adaptive Point Set Registration
Contrary to previous works, we model the underlying structure of the scene as a latent probability distribution, and thereby induce invariance to point set density changes.
Deep Projective 3D Semantic Segmentation
Recent attempts, based on 3D deep learning approaches (3D-CNNs), have achieved below-expected results.
Ranked #15 on
Semantic Segmentation
on Semantic3D
Efficient Multi-Frequency Phase Unwrapping using Kernel Density Estimation
In this paper we introduce an efficient method to unwrap multi-frequency phase estimates for time-of-flight ranging.
