58 citations · 392 across the 30 of their papers we have counts for
10 papers · 1 filter
Real-Time 3D Model Tracking in Color and Depth on a Single CPU Core
Wadim Kehl, Federico Tombari, Slobodan Ilic +1
We present a novel method to track 3D models in color and depth data. To this end, we introduce approximations that accelerate the state-of-the-art in region-based tracking by an o…
CorNet: Generic 3D Corners for 6D Pose Estimation of New Objects without Retraining
Giorgia Pitteri, Slobodan Ilic, Vincent Lepetit
We present a novel approach to the detection and 3D pose estimation of objects in color images. Its main contribution is that it does not require any training phases nor data for n…
On Object Symmetries and 6D Pose Estimation from Images
Giorgia Pitteri, Michaël Ramamonjisoa, Slobodan Ilic +1
Objects with symmetries are common in our daily life and in industrial contexts, but are often ignored in the recent literature on 6D pose estimation from images. In this paper, we…
3D Object Instance Recognition and Pose Estimation Using Triplet Loss with Dynamic Margin
Sergey Zakharov, Wadim Kehl, Benjamin Planche +2
In this paper, we address the problem of 3D object instance recognition and pose estimation of localized objects in cluttered environments using convolutional neural networks. Insp…
3D Local Features for Direct Pairwise Registration
Haowen Deng, Tolga Birdal, Slobodan Ilic
We present a novel, data driven approach for solving the problem of registration of two point cloud scans. Our approach is direct in the sense that a single pair of corresponding l…
HomebrewedDB: RGB-D Dataset for 6D Pose Estimation of 3D Objects
Roman Kaskman, Sergey Zakharov, Ivan Shugurov +1
Among the most important prerequisites for creating and evaluating 6D object pose detectors are datasets with labeled 6D poses. With the advent of deep learning, demand for such da…
DeceptionNet: Network-Driven Domain Randomization
Sergey Zakharov, Wadim Kehl, Slobodan Ilic
We present a novel approach to tackle domain adaptation between synthetic and real data. Instead, of employing "blind" domain randomization, i.e., augmenting synthetic renderings w…
Adversarial Networks for Camera Pose Regression and Refinement
Mai Bui, Christoph Baur, Nassir Navab +2
Despite recent advances on the topic of direct camera pose regression using neural networks, accurately estimating the camera pose of a single RGB image still remains a challenging…
DPOD: 6D Pose Object Detector and Refiner
Sergey Zakharov, Ivan Shugurov, Slobodan Ilic
In this paper we present a novel deep learning method for 3D object detection and 6D pose estimation from RGB images. Our method, named DPOD (Dense Pose Object Detector), estimates…
Generic Primitive Detection in Point Clouds Using Novel Minimal Quadric Fits
Tolga Birdal, Benjamin Busam, Nassir Navab +2
We present a novel and effective method for detecting 3D primitives in cluttered, unorganized point clouds, without axillary segmentation or type specification. We consider the qua…