10 citations · 28 across the 9 of their papers we have counts for
11 papers
MCTS with Refinement for Proposals Selection Games in Scene Understanding
Sinisa Stekovic, Mahdi Rad, Alireza Moradi +2
We propose a novel method applicable in many scene understanding problems that adapts the Monte Carlo Tree Search (MCTS) algorithm, originally designed to learn to play games of hi…
Keypoint Transformer: Solving Joint Identification in Challenging Hands and Object Interactions for Accurate 3D Pose Estimation
Shreyas Hampali, Sayan Deb Sarkar, Mahdi Rad +1
We propose a robust and accurate method for estimating the 3D poses of two hands in close interaction from a single color image. This is a very challenging problem, as large occlus…
MonteFloor: Extending MCTS for Reconstructing Accurate Large-Scale Floor Plans
Sinisa Stekovic, Mahdi Rad, Friedrich Fraundorfer +1
We propose a novel method for reconstructing floor plans from noisy 3D point clouds. Our main contribution is a principled approach that relies on the Monte Carlo Tree Search (MCTS…
ALCN: Adaptive Local Contrast Normalization
Mahdi Rad, Peter M. Roth, Vincent Lepetit
To make Robotics and Augmented Reality applications robust to illumination changes, the current trend is to train a Deep Network with training images captured under many different…
Measuring Generalisation to Unseen Viewpoints, Articulations, Shapes and Objects for 3D Hand Pose Estimation under Hand-Object Interaction
Anil Armagan, Guillermo Garcia-Hernando, Seungryul Baek +32
We study how well different types of approaches generalise in the task of 3D hand pose estimation under single hand scenarios and hand-object interaction. We show that the accuracy…
HOnnotate: A method for 3D Annotation of Hand and Object Poses
Shreyas Hampali, Mahdi Rad, Markus Oberweger +1
We propose a method for annotating images of a hand manipulating an object with the 3D poses of both the hand and the object, together with a dataset created using this method. Our…
Domain Transfer for 3D Pose Estimation from Color Images without Manual Annotations
Mahdi Rad, Markus Oberweger, Vincent Lepetit
We introduce a novel learning method for 3D pose estimation from color images. While acquiring annotations for color images is a difficult task, our approach circumvents this probl…
Making Deep Heatmaps Robust to Partial Occlusions for 3D Object Pose Estimation
Markus Oberweger, Mahdi Rad, Vincent Lepetit
We introduce a novel method for robust and accurate 3D object pose estimation from a single color image under large occlusions. Following recent approaches, we first predict the 2D…
Feature Mapping for Learning Fast and Accurate 3D Pose Inference from Synthetic Images
Mahdi Rad, Markus Oberweger, Vincent Lepetit
We propose a simple and efficient method for exploiting synthetic images when training a Deep Network to predict a 3D pose from an image. The ability of using synthetic images for…
ALCN: Meta-Learning for Contrast Normalization Applied to Robust 3D Pose Estimation
Mahdi Rad, Peter M. Roth, Vincent Lepetit
To be robust to illumination changes when detecting objects in images, the current trend is to train a Deep Network with training images captured under many different lighting cond…
BB8: A Scalable, Accurate, Robust to Partial Occlusion Method for Predicting the 3D Poses of Challenging Objects without Using Depth
Mahdi Rad, Vincent Lepetit
We introduce a novel method for 3D object detection and pose estimation from color images only. We first use segmentation to detect the objects of interest in 2D even in presence o…