• Publications
  • Influence
Deeper Depth Prediction with Fully Convolutional Residual Networks
TLDR
A fully convolutional architecture, encompassing residual learning, to model the ambiguous mapping between monocular images and depth maps is proposed and a novel way to efficiently learn feature map up-sampling within the network is presented.
Model Based Training, Detection and Pose Estimation of Texture-Less 3D Objects in Heavily Cluttered Scenes
TLDR
A framework for automatic modeling, detection, and tracking of 3D objects with a Kinect and shows how to build the templates automatically from 3D models, and how to estimate the 6 degrees-of-freedom pose accurately and in real-time.
SSD-6D: Making RGB-Based 3D Detection and 6D Pose Estimation Great Again
TLDR
A novel method for detecting 3D model instances and estimating their 6D poses from RGB data in a single shot that competes or surpasses current state-of-the-art methods that leverage RGBD data on multiple challenging datasets.
Model globally, match locally: Efficient and robust 3D object recognition
TLDR
A novel method is proposed that creates a global model description based on oriented point pair features and matches that model locally using a fast voting scheme, which allows using much sparser object and scene point clouds, resulting in very fast performance.
Multimodal templates for real-time detection of texture-less objects in heavily cluttered scenes
TLDR
This work presents a method for detecting 3D objects using multi-modalities based on an efficient representation of templates that capture the different modalities, and shows in many experiments on commodity hardware that it significantly outperforms state-of-the-art methods on single modalities.
Gradient Response Maps for Real-Time Detection of Textureless Objects
TLDR
A method for real-time 3D object instance detection that does not require a time-consuming training stage, and can handle untextured objects, and is much faster and more robust with respect to background clutter than current state-of-the-art methods is presented.
Dense image registration through MRFs and efficient linear programming
TLDR
A novel and efficient approach to dense image registration, which does not require a derivative of the employed cost function is introduced, and efficient linear programming using the primal dual principles is considered to recover the lowest potential of the cost function.
Tissue Classification as a Potential Approach for Attenuation Correction in Whole-Body PET/MRI: Evaluation with PET/CT Data
TLDR
A segmented attenuation map with 4 classes derived from CT data had only a small effect on the SUVs of 18F-FDG–avid lesions and did not change the interpretation for any patient, and appears to be practical and valid for MRI-based AC.
3D Pictorial Structures for Multiple Human Pose Estimation
TLDR
A novel 3D pictorial structures (3DPS) model is introduced that infers 3D human body configurations from the authors' reduced state space and is generic and applicable to both single and multiple human pose estimation.
ReLayNet: Retinal Layer and Fluid Segmentation of Macular Optical Coherence Tomography using Fully Convolutional Network
TLDR
A new fully convolutional deep architecture, termed ReLayNet, is proposed for end-to-end segmentation of retinal layers and fluid masses in eye OCT scans, validated on a publicly available benchmark dataset with comparisons against five state-of-the-art segmentation methods.
...
1
2
3
4
5
...