ARTIFICIAL INTELLIGENCE FOR A SAFE AND SUSTAINABLE CONSTRUCTION INDUSTRY

Please check out the article “Artificial intelligence for a safe and sustainable construction industry (dfki.de)” concerning the new EU project HumanTech which is coordinated by Dr. Jason Rambach, head of the Spatial Sensing and Machine Perception team (Augmented Reality/Augmented Vision department, Prof. Didier Stricker) at the German Research Center for Artificial Intelligence (DFKI) in Kaiserslautern.

Augmented Vision @CVPR 2022

DFKI Augmented Vision had a strong presence in the recent CVPR 2022 Conference held on June 19th-23rd, 2022, in New Orleans, USA. The IEEE/CVF Computer Vision and Pattern Recognition Conference (CVPR) is the premier annual computer vision event internationally. Homepage: https://cvpr2022.thecvf.com/ .

Overall, three publications were presented:

1. ZebraPose: Coarse to Fine Surface Encoding for 6DoF Object Pose Estimation
Yongzhi Su, Mahdi Saleh, Torben Fetzer, Jason Raphael Rambach, Nassir Navab, Benjamin Busam, Didier Stricker, Federico Tombari

https://openaccess.thecvf.com/content/CVPR2022/papers/Su_ZebraPose_Coarse_To_Fine_Surface_Encoding_for_6DoF_Object_Pose_CVPR_2022_paper.pdf

2. SOMSI: Spherical Novel View Synthesis with Soft Occlusion Multi-Sphere Images Tewodros A Habtegebrial, Christiano Gava, Marcel Rogge, Didier Stricker, Varun Jampani

https://openaccess.thecvf.com/content/CVPR2022/papers/Habtegebrial_SOMSI_Spherical_Novel_View_Synthesis_With_Soft_Occlusion_Multi-Sphere_Images_CVPR_2022_paper.pdf

3. Unsupervised Anomaly Detection from Time-of-Flight Depth Images
Pascal Schneider, Jason Rambach, Bruno Mirbach , Didier Stricker

https://openaccess.thecvf.com/content/CVPR2022W/PBVS/papers/Schneider_Unsupervised_Anomaly_Detection_From_Time-of-Flight_Depth_Images_CVPRW_2022_paper.pdf

Keynote Presentation by Dr. Jason Rambach in Computer Vision session of the Franco-German Research and Innovation Network event

On June 14th, 2022, Dr. Jason Rambach gave a keynote talk in the Computer Vision session of the  Franco-German Research and Innovation Network event held at the Inria headquarters in Versailles, Paris, France. In the talk, an overview of the current activities of the Spatial Sensing and Machine Perception team at DFKI Augmented Vision was presented.

CVPR 2022: Two papers accepted

We are happy to announce that the Augmented Vision group will present two papers in the upcoming CVPR 2022 Conference from June 19th-23rd in New Orleans, USA. The IEEE/CVF Computer Vision and Pattern Recognition Conference (CVPR) is the premier annual computer vision event internationally. Homepage: https://cvpr2022.thecvf.com/

The two accepted papers are:

Summary: ZebraPose sets a new paradigm on model-based 6DoF object pose estimation by using a binary object surface encoding to train a neural network to predict the locations of model vertices in a coarse to fine manner. ZebraPose shows a major improvement over the state-of-the-art on several datasets of the BOP Object Pose Estimation benchmark.

Preprinthttps://av.dfki.de/publications/zebrapose-coarse-to-fine-surface-encoding-for-6dof-object-pose-estimation/

Contact: Yongzhi Su, Dr. Jason Rambach

Summary: We propose a novel Multi-Sphere Image representation called Soft Occlusion MSI (SOMSI) and efficient rendering technique that produces accurate spherical novel-views from a sparse spherical light-field. SOMSI models appearance features in a smaller set (e.g. 3) of occlusion levels instead of larger number (e.g. 64) of MSI spheres. Experiments on both synthetic and real-world spherical light-fields demonstrate that using SOMSI can provide a good balance between accuracy and run-time. SOMSI view synthesis quality is on-par with state-of-the-art models like NeRF, while being 2 orders of magnitude faster.

For more information, please visit the project page at https://tedyhabtegebrial.github.io/somsi

Contact: Tewodros A Habtegebrial

2 Papers accepted at BMVC 2021 Conference

We are happy to announce that the Augmented Vision group will present 2 papers in the upcoming BMVC 2021 Conference, 22-25 November, 2021:

The British Machine Vision Conference (BMVC) is the British Machine Vision Association (BMVA) annual conference on machine vision, image processing, and pattern recognition. It is one of the major international conferences on computer vision and related areas held in the UK. With increasing popularity and quality, it has established itself as a prestigious event on the vision calendar. Homepage: https://www.bmvc2021.com/  

The 2 accepted papers are:

1.  TICaM: A Time-of-flight In-car Cabin Monitoring Dataset
Authors: Jigyasa Singh Katrolia, Ahmed Elsherif, Hartmut Feld, Bruno Mirbach, Jason Raphael Rambach, Didier Stricker


Summary: TICaM is a Time-of-flight In-car Cabin Monitoring dataset for vehicle interior monitoring using a single wide-angle depth camera. The dataset goes beyond currently available in-car cabin datasets in terms of the ambit of labeled classes, recorded scenarios and annotations provided;  all at the same time. The dataset is available here: https://vizta-tof.kl.dfki.de/

Preprint:  https://www.researchgate.net/publication/355395814_TICaM_A_Time-of-flight_In-car_Cabin_Monitoring_Dataset

Video: https://www.youtube.com/watch?v=aqYUY2JzqHU

Contact: Jason Rambach

2. PlaneRecNet: Multi-Task Learning with Cross-Task Consistency for Piece-Wise Plane Detection and Reconstruction from a Single RGB Image
Authors: Yaxu Xie, Fangwen Shu, Jason Raphael Rambach, Alain Pagani, Didier Stricker

Summary: Piece-wise 3D planar reconstruction provides holistic scene understanding of man-made environments, especially for indoor scenarios. Different from other existing approaches, we start from enforcing cross-task consistency for our multi-task convolutional neural network, PlaneRecNet, which integrates a single-stage instance segmentation network for piece-wise planar segmentation and a depth decoder to reconstruct the scene from a single RGB image.

Preprint: https://www.dfki.de/web/forschung/projekte-publikationen/publikationen-filter/publikation/11908

Contact: Alain Pagani

DFKI AV – Stellantis Collaboration on Radar-Camera Fusion – 2 publications

DFKI Augmented Vision is working with Stellantis on the topic of Radar-Camera Fusion for Automotive Object Detection using Deep Learning since 2020. The collaboration has already led to two publications, in ICCV 2021 (International Conference on Computer Vision – ERCVAD Workshop on “Embedded and Real-World Computer Vision in Autonomous Driving”) and WACV 2022 (Winter Conference on Applications of Computer Vision).

The 2 publications are:

1.  Deployment of Deep Neural Networks for Object Detection on Edge AI Devices with Runtime OptimizationProceedings of the IEEE International Conference on Computer Vision Workshops – ERCVAD Workshop on Embedded and Real-World Computer Vision in Autonomous Driving

Lukas Stefan Stäcker, Juncong Fei, Philipp Heidenreich, Frank Bonarens, Jason Rambach, Didier Stricker, Christoph Stiller

This paper discusses the optimization of neural network based algorithms for object detection based on camera, radar, or lidar data in order to deploy them on an embedded system on a vehicle.

2. Fusion Point Pruning for Optimized 2D Object Detection with Radar-Camera FusionProceedings of the IEEE Winter Conference on Applications of Computer Vision, 2022

Lukas Stefan Stäcker, Juncong Fei, Philipp Heidenreich, Frank Bonarens, Jason Rambach, Didier Stricker, Christoph Stiller

This paper introduces fusion point pruning, a new method to optimize the selection of fusion points within the deep learning network architecture for radar-camera fusion.

Please view the abstract here: Fusion Point Pruning for Optimized 2D Object Detection with Radar-Camera Fusion (dfki.de)

Contact: Dr. Jason Rambach

VIZTA Project Time-of-Flight Camera Datasets Released

As part of the research activities of DFKI Augmented Vision in the VIZTA project (https://www.vizta-ecsel.eu/), two publicly available datasets have been released and are available for download. TIMo dataset is a building indoor monitoring dataset for person detection, person counting, and anomaly detection. TICaM dataset is an automotive in-cabin monitoring dataset with a wide field of view for person detection and segmentation and activity recognition. Real and synthetic images are provided allowing for benchmarking of transfer learning algorithms as well. Both datasets are available here https://vizta-tof.kl.dfki.de/. The publication describing the datasets in detail are available as preprints.

TICaM: https://arxiv.org/pdf/2103.11719.pdf

TIMo: https://arxiv.org/pdf/2108.12196.pdf

Video: https://www.youtube.com/watch?v=xWCor9obttA

Contacts: Dr. Jason Rambach, Dr. Bruno Mirbach

XR for nature and environment survey

On July 29th, 2021, Dr. Jason Rambach presented the survey paper “A Survey on Applications of Augmented, Mixed and Virtual Reality for Nature and Environment” at the 23rd Human Computer Interaction Conference HCI International. The article is the result of a collaboration between DFKI, the Worms University of Applied Sciences and the University of Kaiserslautern.

Abstract: Augmented, virtual and mixed reality (AR/VR/MR) are technologies of great potential due to the engaging and enriching experiences they are capable of providing. However, the possibilities that AR/VR/MR offer in the area of environmental applications are not yet widely explored. In this paper, we present the outcome of a survey meant to discover and classify existing AR/VR/MR applications that can benefit the environment or increase awareness on environmental issues. We performed an exhaustive search over several online publication access platforms and past proceedings of major conferences in the fields of AR/VR/MR. Identified relevant papers were filtered based on novelty, technical soundness, impact and topic relevance, and classified into different categories. Referring to the selected papers, we discuss how the applications of each category are contributing to environmental protection and awareness. We further analyze these approaches as well as possible future directions in the scope of existing and upcoming AR/VR/MR enabling technologies.

Authors: Jason Rambach, Gergana Lilligreen, Alexander Schäfer, Ramya Bankanal, Alexander Wiebel, Didier Stricker

Paper: https://av.dfki.de/publications/a-survey-on-applications-of-augmented-mixed-and-virtual-reality-for-nature-and-environment/

Contact: Jason.Rambach@dfki.de

VIZTA Project 24M Review and public summary

DFKI participates in the VIZTA project, coordinated by ST Micrelectronics, aiming  at developing innovative technologies in the field of optical sensors and laser sources for short to long-range 3D-imaging and to demonstrate their value in several key applications including automotive, security, smart buildings, mobile robotics for smart cities, and industry 4.0. The 24-month review by the EU-commission was completed and a public summary of the project was released, including updates from DFKI Augmented Vision on time-of-flight camera dataset recording and deep learning algorithm development for car in-cabin monitoring and smart building person counting and anomaly detection applications.

Please click here to check out the complete summary: https://www.vizta-ecsel.eu/newsletter-april-2021/

Contact: Dr. Jason Rambach, Dr. Bruno Mirbach

Paper accepted at ICIP 2021

We are happy to announce that our paper “SEMANTIC SEGMENTATION IN DEPTH DATA : A COMPARATIVE EVALUATION OF IMAGE AND POINT CLOUD BASED METHODS” has been accepted for publication at the ICIP 2021 IEEE International Conference on Image Processing which will take place from September 19th to 22nd, 2021 at Anchorage, Alaska, USA.

Abstract: The problem of semantic segmentation from depth images can be addressed by segmenting directly in the image domain or at 3D point cloud level. In this paper, we attempt for the first time to provide a study and experimental comparison of the two approaches. Through experiments on three datasets, namely SUN RGB-D, NYUdV2 and TICaM, we extensively compare various semantic segmentation algorithms, the input to which includes images and point clouds derived from them. Based on this, we offer analysis of the performance and computational cost of these algorithms that can provide guidelines on when each method should be preferred.

Authors: Jigyasa Katrolia, Lars Krämer, Jason Rambach, Bruno Mirbach, Didier Stricker

Paper: https://av.dfki.de/publications/semantic-segmentation-in-depth-data-a-comparative-evaluation-ofimage-and-point-cloud-based-methods/

Contact: Jigyasa_Singh.Katrolia@dfki.de, Jason.Rambach@dfki.de