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Depth-aware Object Segmentation and Grasp Detection for Robotic Picking Tasks 32

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Depth-aware Object Segmentation and Grasp Detection for Robo...

32nd British Machine vision Conference, BMVC 2021

作者： Ainetter, Stefan Böhm, Christoph Dhakate, Rohit Weiss, Stephan Fraundorfer, Friedrich Institute of Computer Graphics and Vision Graz University of Technology Graz Austria Institute of Smart System Technologies University of Klagenfurt Klagenfurt Austria

In this paper, we present a novel deep neural network architecture for joint class-agnostic object segmentation and grasp detection for robotic picking tasks using a parallel-plate gripper. We introduce depth-aware Coordinate Convolution (CoordConv), a method to increase accuracy for point proposal based object instance segmentation in complex scenes without adding any additional network parameters or computation complexity. Depth-aware CoordConv uses depth data to extract prior information about the location of an object to achieve highly accurate object instance segmentation. These resulting segmentation masks, combined with predicted grasp candidates, lead to a complete scene description for grasping using a parallel-plate gripper. We evaluate the accuracy of grasp detection and instance segmentation on challenging robotic picking datasets, namely Siléane and OCID_grasp, and show the benefit of joint grasp detection and segmentation on a real-world robotic picking task. © 2021. The copyright of this document resides with its authors.

关键词： Grippers

HOC-Search: Efficient CAD Model and Pose Retrieval From RGB-D Scans

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HOC-Search: Efficient CAD Model and Pose Retrieval From RGB-...

International Conference on 3D Imaging, Modeling, Processing, Visualization and Transmission (3DIMPVT)

作者： Stefan Ainetter Sinisa Stekovic Friedrich Fraundorfer Vincent Lepetit Institute for Computer Graphics and Vision Graz University of Technology Graz Austria LIGM École des Ponts Univ Gustave Eiffel CNRS Marne-la-Vallée France

ISBN: (数字)9798350362459

ISBN: (纸本)9798350362466

We present an automated and efficient approach for retrieving high-quality CAD models of objects and their poses in a scene captured by a moving RGB-D camera. We first investigate various objective functions to measure similarity between a candidate CAD object model and the available data, and the best objective function appears to be a ”render-and-compare” method comparing depth and mask rendering. We thus introduce a fast-search method that approximates an exhaustive search based on this objective function for simultaneously retrieving the object category, a CAD model, and the pose of an object given an approximate 3D bounding box. This method involves a search tree that organizes the CAD models and object properties including object category and pose for fast retrieval and an algorithm inspired by Monte Carlo Tree Search, that efficiently searches this tree. We show that this method retrieves CAD models that fit the real objects very well, with a speed-up factor of 10$\times$ to 120$\times$ compared to exhaustive search. We used our method to automatically retrieve CAD models for objects in the ScanNet dataset; our annotations are available at https://***/stefanainetter/SCANnotateDataset.

关键词： Training Point cloud compression Solid modeling Three-dimensional displays Monte Carlo methods Object detection Search problems

FCDSN-DC: An Accurate and Lightweight Convolutional Neural Network for Stereo Estimation with Depth Completion

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arXiv 2022年

作者： Hirner, Dominik Fraundorfer, Friedrich Graz University of Technology Austria Institute for Computer Graphics and Vision Austria

We propose an accurate and lightweight convolutional neural network for stereo estimation with depth completion. We name this method fully-convolutional deformable similarity network with depth completion (FCDSN-DC). This method extends FC-DCNN by improving the feature extractor, adding a network structure for training highly accurate similarity functions and a network structure for filling inconsistent disparity estimates. The whole method consists of three parts. The first part consists of fully-convolutional densely connected layers that computes expressive features of rectified image pairs. The second part of our network learns highly accurate similarity functions between this learned features. It consists of densely-connected convolution layers with a deformable convolution block at the end to further improve the accuracy of the results. After this step an initial disparity map is created and the left-right consistency check is performed in order to remove inconsistent points. The last part of the network then uses this input together with the corresponding left RGB image in order to train a network that fills in the missing measurements. Consistent depth estimations are gathered around invalid points and are parsed together with the RGB points into a shallow CNN network structure in order to recover the missing values. We evaluate our method on challenging real world indoor and outdoor scenes, in particular Middlebury, KITTI and ETH3D were it produces competitive results. We furthermore show that this method generalizes well and is well suited for many applications without the need of further training. The code of our full framework is available at: https://***/thedodo/FCDSN-DC. © 2022, CC BY.

关键词： Convolution

Gaussian Process Emulators for Few-Shot Segmentation in Cardiac MRI 15th

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Gaussian Process Emulators for Few-Shot Segmentation in Ca...

15th International Workshop on Statistical Atlases and Computational Models of the Heart, STACOM 2024, Held in Conjunction with MICCAI 2024

作者： Viti, Bruno Thaler, Franz Kapper, Kathrin Lisa Urschler, Martin Holler, Martin Karabelas, Elias Department of Mathematics and Scientific Computing University of Graz Graz Austria Institute for Medical Informatics Statistics and Documentation Medical University of Graz Graz Austria Institute of Computer Graphics and Vision Graz University of Technology Graz Austria Gottfried Schatz Research Center: Medical Physics and Biophysics Medical University of Graz Graz Austria BioTechMed-Graz Graz Austria

ISBN: (纸本)9783031877551

Segmentation of cardiac magnetic resonance images (MRI) is crucial for the analysis and assessment of cardiac function, helping to diagnose and treat various cardiovascular diseases. Most recent techniques rely on deep learning and usually require an extensive amount of labeled data. To overcome this problem, few-shot learning has the capability of reducing data dependency on labeled data. In this work, we introduce a new method that merges few-shot learning with a U-Net architecture and Gaussian Process Emulators (GPEs), enhancing data integration from a support set for improved performance. GPEs are trained to learn the relation between the support images and the corresponding masks in latent space, facilitating the segmentation of unseen query images given only a small labeled support set at inference. We test our model with the M&Ms-2 public dataset to assess its ability to segment the heart in cardiac magnetic resonance imaging from different orientations, and compare it with state-of-the-art unsupervised and few-shot methods. Our architecture shows higher DICE coefficients compared to these methods, especially in the more challenging setups where the size of the support set is considerably small. The code is available on GitLab (https://***/bruno_viti/gpe_4_cardiac_fss). © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Gaussian distribution

Enhanced Data Augmentation Using Synthetic Data for Brain Tumour Segmentation 1

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Challenge on Brain Tumor Segmentation, BraTS 2023, International Challenge on Cross-Modality Domain Adaptation for Medical Image Segmentation, CrossMoDA 2023, held in conjunction with the Medical Image Computing for computer Assisted Intervention Conference, MICCAI 2023

作者： Ferreira, André Solak, Naida Li, Jianning Dammann, Philipp Kleesiek, Jens Alves, Victor Egger, Jan Center Algoritmi/LASI University of Minho Braga4710-057 Portugal Computer Algorithms for Medicine Laboratory Graz Austria University Medicine Essen Girardetstraße 2 Essen45131 Germany University Medicine Essen Hufelandstraße 55 Essen45147 Germany Partner Site Essen Hufelandstraße 55 Essen45147 Germany Institute of Computer Graphics and Vision Graz University of Technology Inffeldgasse 16 Graz8010 Austria Department of Neurosurgery and Spine Surgery University Hospital Essen Essen Germany

ISBN: (数字)9783031761638

ISBN: (纸本)9783031761621

Deep Learning is the state-of-the-art technology for segmenting brain tumours. However, this requires a lot of high-quality data, which is difficult to obtain, especially in the medical field. Therefore, our solutions address this problem by using unconventional mechanisms for data augmentation. Generative adversarial networks and registration are used to massively increase the amount of available samples for training three different deep learning models for adult glioma segmentation, the Task 1 of the BraTS 2023 challenge. The first model is the standard nnU-Net, the second is the Swin UNETR and the third is the winning solution of the BraTS 2021 Challenge. The entire pipeline is built on the nnU-Net implementation, except for the generation of the synthetic data. The use of convolutional algorithms and transformers is able to fill each other’s knowledge gaps. Our solution achieves the lesion-wise Dice metric of 0.8851, 0.8719, 0.8685 and lesion-wise HD95 of 22.838, 22.974, 16.713 (whole tumour, tumour core and enhancing tumour) on the testing cohort. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Generative adversarial networks

Multi-Source and Multi-Sequence Myocardial Pathology Segmentation Using a Cascading Refinement CNN

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arXiv 2024年

作者： Thaler, Franz Štern, Darko Plank, Gernot Urschler, Martin Gottfried Schatz Research Center: Medical Physics and Biophysics Medical University of Graz Graz Austria Institute of Computer Graphics and Vision Graz University of Technology Graz Austria AVL List GmbH Graz Austria Institute for Medical Informatics Statistics and Documentation Medical University of Graz Graz Austria

Myocardial infarction (MI) is one of the most prevalent cardiovascular diseases and consequently, a major cause for mortality and morbidity worldwide. Accurate assessment of myocardial tissue viability for post-MI patients is critical for diagnosis and treatment planning, e.g. allowing surgical revascularization, or to determine the risk of adverse cardiovascular events in the future. Fine-grained analysis of the myocardium and its surrounding anatomical structures can be performed by combining the information obtained from complementary medical imaging techniques. In this work, we use late gadolinium enhanced (LGE) magnetic resonance (MR), T2-weighted (T2) MR and balanced steady-state free precession (bSSFP) cine MR in order to semantically segment the left and right ventricle, healthy and scarred myocardial tissue, as well as edema. To this end, we propose the Multi-Sequence Cascading Refinement CNN (MS-CaRe-CNN), a 2-stage CNN cascade that receives multi-sequence data and generates predictions of the anatomical structures of interest without considering tissue viability at Stage 1. The prediction of Stage 1 is then further refined in Stage 2, where the model additionally distinguishes myocardial tissue based on viability, i.e. healthy, scarred and edema regions. Our proposed method is set up as a 5-fold ensemble and semantically segments scar tissue achieving 62.31% DSC and 82.65% precision, as well as 63.78% DSC and 87.69% precision for the combined scar and edema region. These promising results for such small and challenging structures confirm that MS-CaRe-CNN is well-suited to generate semantic segmentations to assess the viability of myocardial tissue, enabling downstream tasks like personalized therapy planning. © 2024, CC BY-SA.

关键词： Medical imaging

Computed Tomography Reconstruction Using Generative Energy-Based Priors

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arXiv 2022年

作者： Zach, Martin Kobler, Erich Pock, Thomas Institute of Computer Graphics and Vision Graz University of Technology Graz8010 Austria Institute of Computer Graphics Johannes Kepler University Linz Linz4040 Austria

In the past decades, Computed Tomography (CT) has established itself as one of the most important imaging techniques in medicine. Today, the applicability of CT is only limited by the deposited radiation dose, reduction of which manifests in noisy or incomplete measurements. Thus, the need for robust reconstruction algorithms arises. In this work, we learn a parametric regularizer with a global receptive field by maximizing it’s likelihood on reference CT data. Due to this unsupervised learning strategy, our trained regularizer truly represents higher-level domain statistics, which we empirically demonstrate by synthesizing CT images. Moreover, this regularizer can easily be applied to different CT reconstruction problems by embedding it in a variational framework, which increases flexibility and interpretability compared to feed-forward learning-based approaches. In addition, the accompanying probabilistic perspective enables experts to explore the full posterior distribution and may quantify uncertainty of the reconstruction approach. We apply the regularizer to limited-angle and few-view CT reconstruction problems, where it outperforms traditional reconstruction algorithms by a large margin. © 2022, CC BY.

关键词： Image reconstruction

vision-Language Guidance for LiDAR-based Unsupervised 3D Object Detection

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arXiv 2024年

作者： Fruhwirth-Reisinger, Christian Lin, Wei Malić, Dušan Bischof, Horst Possegger, Horst Christian Doppler Laboratory for Embedded Machine Learning Austria Institute of Computer Graphics and Vision Graz University of Technology Austria Institute for Machine Learning Johannes Kepler University Linz Austria

Accurate 3D object detection in LiDAR point clouds is crucial for autonomous driving systems. To achieve state-of-the-art performance, the supervised training of detectors requires large amounts of human-annotated data, which is expensive to obtain and restricted to predefined object categories. To mitigate manual labeling efforts, recent unsupervised object detection approaches generate class-agnostic pseudo-labels for moving objects, subsequently serving as supervision signal to bootstrap a detector. Despite promising results, these approaches do not provide class labels or generalize well to static objects. Furthermore, they are mostly restricted to data containing multiple drives from the same scene or images from a precisely calibrated and synchronized camera setup. To overcome these limitations, we propose a vision-language-guided unsupervised 3D detection approach that operates exclusively on LiDAR point clouds. We transfer CLIP knowledge to classify point clusters of static and moving objects, which we discover by exploiting the inherent spatio-temporal information of LiDAR point clouds for clustering, tracking, as well as box and label refinement. Our approach outperforms state-of-the-art unsupervised 3D object detectors on the Waymo Open Dataset (+23 AP3D) and Argoverse 2 (+7.9 AP3D) and provides class labels not solely based on object size assumptions, marking a significant advancement in the field. Code will be available at https://***/chreisinger/ViLGOD. © 2024, CC BY-NC-SA.

关键词： Visual languages

Teeth Localization and Lesion Segmentation in CBCT Images using SpatialConfiguration-Net and U-Net

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arXiv 2023年

作者： Hadzic, Arnela Kirnbauer, Barbara Štern, Darko Urschler, Martin Institute for Medical Informatics Statistics and Documentation Medical University of Graz Graz Austria Department of Dental Medicine and Oral Health Medical University of Graz Graz Austria Institute of Computer Graphics and Vision Graz University of Technology Graz Austria

The localization of teeth and segmentation of periapical lesions in cone-beam computed tomography (CBCT) images are crucial tasks for clinical diagnosis and treatment planning, which are often time-consuming and require a high level of expertise. However, automating these tasks is challenging due to variations in shape, size, and orientation of lesions, as well as similar topologies among teeth. Moreover, the small volumes occupied by lesions in CBCT images pose a class imbalance problem that needs to be addressed. In this study, we propose a deep learning-based method utilizing two convolutional neural networks: the SpatialConfiguration-Net (SCN) and a modified version of the U-Net. The SCN accurately predicts the coordinates of all teeth present in an image, enabling precise cropping of teeth volumes that are then fed into the U-Net which detects lesions via segmentation. To address class imbalance, we compare the performance of three reweighting loss functions. After evaluation on 144 CBCT images, our method achieves a 97.3% accuracy for teeth localization, along with a promising sensitivity and specificity of 0.97 and 0.88, respectively, for subsequent lesion detection. © 2023, CC BY-SA.

关键词： Convolutional neural networks