3,037 research outputs found

    Structured Light-Based 3D Reconstruction System for Plants.

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    Camera-based 3D reconstruction of physical objects is one of the most popular computer vision trends in recent years. Many systems have been built to model different real-world subjects, but there is lack of a completely robust system for plants. This paper presents a full 3D reconstruction system that incorporates both hardware structures (including the proposed structured light system to enhance textures on object surfaces) and software algorithms (including the proposed 3D point cloud registration and plant feature measurement). This paper demonstrates the ability to produce 3D models of whole plants created from multiple pairs of stereo images taken at different viewing angles, without the need to destructively cut away any parts of a plant. The ability to accurately predict phenotyping features, such as the number of leaves, plant height, leaf size and internode distances, is also demonstrated. Experimental results show that, for plants having a range of leaf sizes and a distance between leaves appropriate for the hardware design, the algorithms successfully predict phenotyping features in the target crops, with a recall of 0.97 and a precision of 0.89 for leaf detection and less than a 13-mm error for plant size, leaf size and internode distance

    Active Image-based Modeling with a Toy Drone

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    Image-based modeling techniques can now generate photo-realistic 3D models from images. But it is up to users to provide high quality images with good coverage and view overlap, which makes the data capturing process tedious and time consuming. We seek to automate data capturing for image-based modeling. The core of our system is an iterative linear method to solve the multi-view stereo (MVS) problem quickly and plan the Next-Best-View (NBV) effectively. Our fast MVS algorithm enables online model reconstruction and quality assessment to determine the NBVs on the fly. We test our system with a toy unmanned aerial vehicle (UAV) in simulated, indoor and outdoor experiments. Results show that our system improves the efficiency of data acquisition and ensures the completeness of the final model.Comment: To be published on International Conference on Robotics and Automation 2018, Brisbane, Australia. Project Page: https://huangrui815.github.io/active-image-based-modeling/ The author's personal page: http://www.sfu.ca/~rha55

    Building with Drones: Accurate 3D Facade Reconstruction using MAVs

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    Automatic reconstruction of 3D models from images using multi-view Structure-from-Motion methods has been one of the most fruitful outcomes of computer vision. These advances combined with the growing popularity of Micro Aerial Vehicles as an autonomous imaging platform, have made 3D vision tools ubiquitous for large number of Architecture, Engineering and Construction applications among audiences, mostly unskilled in computer vision. However, to obtain high-resolution and accurate reconstructions from a large-scale object using SfM, there are many critical constraints on the quality of image data, which often become sources of inaccuracy as the current 3D reconstruction pipelines do not facilitate the users to determine the fidelity of input data during the image acquisition. In this paper, we present and advocate a closed-loop interactive approach that performs incremental reconstruction in real-time and gives users an online feedback about the quality parameters like Ground Sampling Distance (GSD), image redundancy, etc on a surface mesh. We also propose a novel multi-scale camera network design to prevent scene drift caused by incremental map building, and release the first multi-scale image sequence dataset as a benchmark. Further, we evaluate our system on real outdoor scenes, and show that our interactive pipeline combined with a multi-scale camera network approach provides compelling accuracy in multi-view reconstruction tasks when compared against the state-of-the-art methods.Comment: 8 Pages, 2015 IEEE International Conference on Robotics and Automation (ICRA '15), Seattle, WA, US

    Multi-view 3D data acquisition using a single uncoded light pattern

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    This work is part of the project ’3D-Head’ funded by the Malta Council for Science and Technology under Research Grant No. RTDI-2004-034.This research concerns the acquisition of 3-dimensional data from images for the purpose of modeling a person's head. This paper proposes an approach for acquiring the 3-dimensional reconstruction using a multiple stereo camera vision platform and a combination of passive and active lighting techniques. The proposed one-shot active lighting method projects a single, binary dot pattern, hence ensuring the suitability of the method to reconstruct dynamic scenes. Contrary to the conventional spatial neighborhood coding techniques, this approach matches corresponding spots between image pairs by exploiting solely the redundant data available in the multiple camera images. This produces an initial, sparse reconstruction, which is then used to guide a passive lighting technique to obtain a dense 3-dimensional representation of the object of interest. The results obtained reveal the robustness of the projected pattern and the spot matching algorithm, and a decrease in the number of false matches in the 3-dimensional dense reconstructions, particularly in smooth and textureless regions on the human face.peer-reviewe

    Optical techniques for 3D surface reconstruction in computer-assisted laparoscopic surgery

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    One of the main challenges for computer-assisted surgery (CAS) is to determine the intra-opera- tive morphology and motion of soft-tissues. This information is prerequisite to the registration of multi-modal patient-specific data for enhancing the surgeon’s navigation capabilites by observ- ing beyond exposed tissue surfaces and for providing intelligent control of robotic-assisted in- struments. In minimally invasive surgery (MIS), optical techniques are an increasingly attractive approach for in vivo 3D reconstruction of the soft-tissue surface geometry. This paper reviews the state-of-the-art methods for optical intra-operative 3D reconstruction in laparoscopic surgery and discusses the technical challenges and future perspectives towards clinical translation. With the recent paradigm shift of surgical practice towards MIS and new developments in 3D opti- cal imaging, this is a timely discussion about technologies that could facilitate complex CAS procedures in dynamic and deformable anatomical regions

    Fully Reversed Engineering: streamlining 3D component digitization, modification, and reproduction

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    The availability of rapid prototyping enhances a designer’s creativity and speed, enabling quicker development of new products. However, because this process relies heavily on CAD models it can often be time costly and inefficient when a component is needed urgently in the field. This paper proposes a method to seamlessly integrate the digitization of existing objects with the rapid prototyping process. Our technique makes use of multiple structured-light techniques in conjunction with photogrammetry to build a more efficient means of product development. This combination of methods allows our developed application to rapidly scan an entire object using inexpensive hardware
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