58 citations · 392 across the 30 of their papers we have counts for
39 papers
GeoTransformer: Fast and Robust Point Cloud Registration with Geometric Transformer
Zheng Qin, Hao Yu, Changjian Wang +5
We study the problem of extracting accurate correspondences for point cloud registration. Recent keypoint-free methods have shown great potential through bypassing the detection of…
On the Importance of Accurate Geometry Data for Dense 3D Vision Tasks
HyunJun Jung, Patrick Ruhkamp, Guangyao Zhai +10
Learning-based methods to solve dense 3D vision problems typically train on 3D sensor data. The respectively used principle of measuring distances provides advantages and drawbacks…
Rotation-Invariant Transformer for Point Cloud Matching
Hao Yu, Zheng Qin, Ji Hou +4
The intrinsic rotation invariance lies at the core of matching point clouds with handcrafted descriptors. However, it is widely despised by recent deep matchers that obtain the rot…
What can we learn about a generated image corrupting its latent representation?
Agnieszka Tomczak, Aarushi Gupta, Slobodan Ilic +2
Generative adversarial networks (GANs) offer an effective solution to the image-to-image translation problem, thereby allowing for new possibilities in medical imaging. They can tr…
RIGA: Rotation-Invariant and Globally-Aware Descriptors for Point Cloud Registration
Hao Yu, Ji Hou, Zheng Qin +5
Successful point cloud registration relies on accurate correspondences established upon powerful descriptors. However, existing neural descriptors either leverage a rotation-varian…
Multi-View Object Pose Refinement With Differentiable Renderer
Ivan Shugurov, Ivan Pavlov, Sergey Zakharov +1
This paper introduces a novel multi-view 6 DoF object pose refinement approach focusing on improving methods trained on synthetic data. It is based on the DPOD detector, which prod…
DPODv2: Dense Correspondence-Based 6 DoF Pose Estimation
Ivan Shugurov, Sergey Zakharov, Slobodan Ilic
We propose a three-stage 6 DoF object detection method called DPODv2 (Dense Pose Object Detector) that relies on dense correspondences. We combine a 2D object detector with a dense…
Is my Depth Ground-Truth Good Enough? HAMMER -- Highly Accurate Multi-Modal Dataset for DEnse 3D Scene Regression
HyunJun Jung, Patrick Ruhkamp, Guangyao Zhai +9
Depth estimation is a core task in 3D computer vision. Recent methods investigate the task of monocular depth trained with various depth sensor modalities. Every sensor has its adv…
OSOP: A Multi-Stage One Shot Object Pose Estimation Framework
Ivan Shugurov, Fu Li, Benjamin Busam +1
We present a novel one-shot method for object detection and 6 DoF pose estimation, that does not require training on target objects. At test time, it takes as input a target image…
NeRF-Pose: A First-Reconstruct-Then-Regress Approach for Weakly-supervised 6D Object Pose Estimation
Fu Li, Hao Yu, Ivan Shugurov +3
Pose estimation of 3D objects in monocular images is a fundamental and long-standing problem in computer vision. Existing deep learning approaches for 6D pose estimation typically…
CoFiNet: Reliable Coarse-to-fine Correspondences for Robust Point Cloud Registration
Hao Yu, Fu Li, Mahdi Saleh +2
We study the problem of extracting correspondences between a pair of point clouds for registration. For correspondence retrieval, existing works benefit from matching sparse keypoi…
DistillPose: Lightweight Camera Localization Using Auxiliary Learning
Yehya Abouelnaga, Mai Bui, Slobodan Ilic
We propose a lightweight retrieval-based pipeline to predict 6DOF camera poses from RGB images. Our pipeline uses a convolutional neural network (CNN) to encode a query image as a…
Deep Bingham Networks: Dealing with Uncertainty and Ambiguity in Pose Estimation
Haowen Deng, Mai Bui, Nassir Navab +3
In this work, we introduce Deep Bingham Networks (DBN), a generic framework that can naturally handle pose-related uncertainties and ambiguities arising in almost all real life app…
3D Object Detection and Pose Estimation of Unseen Objects in Color Images with Local Surface Embeddings
Giorgia Pitteri, Aurélie Bugeau, Slobodan Ilic +1
We present an approach for detecting and estimating the 3D poses of objects in images that requires only an untextured CAD model and no training phase for new objects. Our approach…
6D Camera Relocalization in Ambiguous Scenes via Continuous Multimodal Inference
Mai Bui, Tolga Birdal, Haowen Deng +4
We present a multimodal camera relocalization framework that captures ambiguities and uncertainties with continuous mixture models defined on the manifold of camera poses. In highl…
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…