16 citations · 29 across the 26 of their papers we have counts for
28 papers
BIMStruct3D: A Fully Automated Hybrid Learning Scan-to-BIM Pipeline with Integrated Topology Refinement
Mahdi Chamseddine, Fabian Kaufmann, Marius Schellen +3
Automatic generation of Building Information Models (BIM) from building scans is a key challenge in architecture and construction. We present a modular pipeline for generating IFC-…
Conditional Attribution for Root Cause Analysis in Time-Series Anomaly Detection
Shashank Mishra, Karan Patil, Cedric Schockaert +2
Root cause analysis (RCA) for time-series anomaly detection is critical for the reliable operation of complex real-world systems. Existing explanation methods often rely on unreali…
JOPP-3D: Joint Open Vocabulary Semantic Segmentation on Point Clouds and Panoramas
Sandeep Inuganti, Hideaki Kanayama, Kanta Shimizu +4
Semantic segmentation across visual modalities such as 3D point clouds and panoramic images remains a challenging task, primarily due to the scarcity of annotated data and the limi…
Conditional Compatibility Learning for Context-Dependent Anomaly Detection
Shashank Mishra, Didier Stricker, Jason Rambach
Anomaly detection usually assumes that abnormality is an intrinsic property of an observation. A defect is a defect, and a rare object is rare, regardless of where it appears. Many…
PanoSAMic: Panoramic Image Segmentation from SAM Feature Encoding and Dual View Fusion
Mahdi Chamseddine, Didier Stricker, Jason Rambach
Existing image foundation models are not optimized for spherical images having been trained primarily on perspective images. PanoSAMic integrates the pre-trained Segment Anything (…
IMKD: Intensity-Aware Multi-Level Knowledge Distillation for Camera-Radar Fusion
Shashank Mishra, Karan Patil, Didier Stricker +1
High-performance Radar-Camera 3D object detection can be achieved by leveraging knowledge distillation without using LiDAR at inference time. However, existing distillation methods…
DriverGaze360: OmniDirectional Driver Attention with Object-Level Guidance
Shreedhar Govil, Didier Stricker, Jason Rambach
Predicting driver attention is a critical problem for developing explainable autonomous driving systems and understanding driver behavior in mixed human-autonomous vehicle traffic…
A Multi-Camera Vision-Based Approach for Fine-Grained Assembly Quality Control
Ali Nazeri, Shashank Mishra, Achim Wagner +3
Quality control is a critical aspect of manufacturing, particularly in ensuring the proper assembly of small components in production lines. Existing solutions often rely on single…
ParticleSAM: Small Particle Segmentation for Material Quality Monitoring in Recycling Processes
Yu Zhou, Pelle Thielmann, Ayush Chamoli +3
The construction industry represents a major sector in terms of resource consumption. Recycled construction material has high reuse potential, but quality monitoring of the aggrega…
JENGA: Object selection and pose estimation for robotic grasping from a stack
Sai Srinivas Jeevanandam, Sandeep Inuganti, Shreedhar Govil +2
Vision-based robotic object grasping is typically investigated in the context of isolated objects or unstructured object sets in bin picking scenarios. However, there are several s…
Resolving Symmetry Ambiguity in Correspondence-based Methods for Instance-level Object Pose Estimation
Yongliang Lin, Yongzhi Su, Sandeep Inuganti +5
Estimating the 6D pose of an object from a single RGB image is a critical task that becomes additionally challenging when dealing with symmetric objects. Recent approaches typicall…
HiPose: Hierarchical Binary Surface Encoding and Correspondence Pruning for RGB-D 6DoF Object Pose Estimation
Yongliang Lin, Yongzhi Su, Praveen Nathan +7
In this work, we present a novel dense-correspondence method for 6DoF object pose estimation from a single RGB-D image. While many existing data-driven methods achieve impressive p…
Cross-Dataset Experimental Study of Radar-Camera Fusion in Bird's-Eye View
Lukas Stäcker, Philipp Heidenreich, Jason Rambach +1
By exploiting complementary sensor information, radar and camera fusion systems have the potential to provide a highly robust and reliable perception system for advanced driver ass…
Single Frame Semantic Segmentation Using Multi-Modal Spherical Images
Suresh Guttikonda, Jason Rambach
In recent years, the research community has shown a lot of interest to panoramic images that offer a 360-degree directional perspective. Multiple data modalities can be fed, and co…
U-RED: Unsupervised 3D Shape Retrieval and Deformation for Partial Point Clouds
Yan Di, Chenyangguang Zhang, Ruida Zhang +6
In this paper, we propose U-RED, an Unsupervised shape REtrieval and Deformation pipeline that takes an arbitrary object observation as input, typically captured by RGB images or s…
Achieving RGB-D level Segmentation Performance from a Single ToF Camera
Pranav Sharma, Jigyasa Singh Katrolia, Jason Rambach +3
Depth is a very important modality in computer vision, typically used as complementary information to RGB, provided by RGB-D cameras. In this work, we show that it is possible to o…
RC-BEVFusion: A Plug-In Module for Radar-Camera Bird's Eye View Feature Fusion
Lukas Stäcker, Shashank Mishra, Philipp Heidenreich +2
Radars and cameras belong to the most frequently used sensors for advanced driver assistance systems and automated driving research. However, there has been surprisingly little res…
OPA-3D: Occlusion-Aware Pixel-Wise Aggregation for Monocular 3D Object Detection
Yongzhi Su, Yan Di, Fabian Manhardt +5
Despite monocular 3D object detection having recently made a significant leap forward thanks to the use of pre-trained depth estimators for pseudo-LiDAR recovery, such two-stage me…
Unsupervised Anomaly Detection from Time-of-Flight Depth Images
Pascal Schneider, Jason Rambach, Bruno Mirbach +1
Video anomaly detection (VAD) addresses the problem of automatically finding anomalous events in video data. The primary data modalities on which current VAD systems work on are mo…
ZebraPose: Coarse to Fine Surface Encoding for 6DoF Object Pose Estimation
Yongzhi Su, Mahdi Saleh, Torben Fetzer +5
Establishing correspondences from image to 3D has been a key task of 6DoF object pose estimation for a long time. To predict pose more accurately, deeply learned dense maps replace…
IKEA Object State Dataset: A 6DoF object pose estimation dataset and benchmark for multi-state assembly objects
Yongzhi Su, Mingxin Liu, Jason Rambach +3
Utilizing 6DoF(Degrees of Freedom) pose information of an object and its components is critical for object state detection tasks. We present IKEA Object State Dataset, a new datase…
PlaneRecNet: Multi-Task Learning with Cross-Task Consistency for Piece-Wise Plane Detection and Reconstruction from a Single RGB Image
Yaxu Xie, Fangwen Shu, Jason Rambach +2
Piece-wise 3D planar reconstruction provides holistic scene understanding of man-made environments, especially for indoor scenarios. Most recent approaches focused on improving the…
TIMo -- A Dataset for Indoor Building Monitoring with a Time-of-Flight Camera
Pascal Schneider, Yuriy Anisimov, Raisul Islam +4
We present TIMo (Time-of-flight Indoor Monitoring), a dataset for video-based monitoring of indoor spaces captured using a time-of-flight (ToF) camera. The resulting depth videos f…
Deployment of Deep Neural Networks for Object Detection on Edge AI Devices with Runtime Optimization
Lukas Stäcker, Juncong Fei, Philipp Heidenreich +4
Deep neural networks have proven increasingly important for automotive scene understanding with new algorithms offering constant improvements of the detection performance. However,…