3D Point Cloud Reconstruction Research Articles

A kinect-based parking assistance system

This work presents an IR-based system for parking assistance and obstacle detection in the automotive field that employs the Microsoft Kinect camera for fast 3D point cloud reconstruction. In contrast to previous research that attempts to explicitly identify obstacles, the proposed system aims to detect "reachable regions" of the environment, i.e., those regions where the vehicle can drive to from its current position. A user-friendly 2D traversability grid of cells is generated and used as a visual aid for parking assistance. Given a raw 3D point cloud, first each point is mapped into individual cells, then, the elevation information is used within a graph-based algorithm to label a given cell as traversable or non-traversable. Following this rationale, positive and negative obstacles, as well as unknown regions can be implicitly detected. Additionally, no flat-world assumption is required. Experimental results, obtained from the system in typical parking scenarios, are presented showing its effectiveness for scene interpretation and detection of several types of obstacle.

3D global estimation and augmented reality visualization of intra-operative X-ray dose.

The growing use of image-guided minimally-invasive surgical procedures is confronting clinicians and surgical staff with new radiation exposure risks from X-ray imaging devices. The accurate estimation of intra-operative radiation exposure can increase staff awareness of radiation exposure risks and enable the implementation of well-adapted safety measures. The current surgical practice of wearing a single dosimeter at chest level to measure radiation exposure does not provide a sufficiently accurate estimation of radiation absorption throughout the body. In this paper, we propose an approach that combines data from wireless dosimeters with the simulation of radiation propagation in order to provide a global radiation risk map in the area near the X-ray device. We use a multi-camera RGBD system to obtain a 3D point cloud reconstruction of the room. The positions of the table, C-arm and clinician are then used 1) to simulate the propagation of radiation in a real-world setup and 2) to overlay the resulting 3D risk-map onto the scene in an augmented reality manner. By using real-time wireless dosimeters in our system, we can both calibrate the simulation and validate its accuracy at specific locations in real-time. We demonstrate our system in an operating room equipped with a robotised X-ray imaging device and validate the radiation simulation on several X-ray acquisition setups.

Time-of-Flight camera based 3D point cloud reconstruction of a car

Modern car wash systems are generally controlled by light barriers and power measurement sensors. These sensors are directly mounted on the movable parts of the system which result in a slow regulation, leading to non-optimal performance, e.g. in terms of energy, water and detergent consumption or regarding the quality of the result.This paper presents an alternative approach, based on online automatic acquisition of 3D vehicle models, thus allowing for an a priori optimization of the washing process in order to achieve a better overall performance of the car wash system. Technically, the approach uses merged and synchronized data acquired by a multiple Time-of-Flight (ToF) camera setup. The proposed processing concept handles the range data sets acquired online, and performs data preprocessing, registration and fusion, as well as geometry extraction. The realized system is based on Graphics Processing Units (GPUs) in order to achieve a sufficient temporal processing performance.Furthermore, details are given showing how to solve the main challenges related to point cloud data processing, especially due to low image resolution, the influences of specific environment conditions, e.g. variations in coating and material reflectivity. The data registration is twofold, on the one hand the multiple ToF images are registered using the pre-calibrated camera system (extrinsic parameters), whereas the inter-frame registration is performed using a point-to-plane based iterative closest point (ICP) on the other hand. Finally, the merged data is integrated in a GPU data structure to allow a fast and efficient 3D point cloud reconstruction.