دانلود رایگان مقاله رویکرد ترکیب تصاعدی ابرهای نقطه تصرف شده برای به روز رسانی BIM

abstract

Building information models (BIMs) provide opportunities to serve as an information repository to store and deliver as-built information. Since a building is not always constructed exactly as the design information specifies, there will be discrepancies between a BIM created in the design phase (called as-designed BIM) and the as-built conditions. Point clouds captured by laser scans can be used as a reference to update an as-designed BIM into an as-built BIM (i.e., the BIM that captures the as-built information). Occlusions and construction progress prevent a laser scan performed at a single point in time to capture a complete view of building components. Progressively scanning a building during the construction phase and combining the progressively captured point cloud data together can provide the geometric information missing in the point cloud data captured previously. However, combining all point cloud data will result in large file sizes and might not always guarantee additional building component information. This paper provides the details of an approach developed to help engineers decide on which progressively captured point cloud data to combine in order to get more geometric information and eliminate large file sizes due to redundant point clouds

6. Conclusion

Progressively captured and registered point clouds provide opportunities to capture a more complete view of the building components over time. A unique challenge of using registered point clouds is that registering point clouds that contain overlapping information might increase the difficulty of storing and processing registered point clouds due to file size. This paper presented an approach that evaluates the information contained in point clouds and supports the decision on which point clouds should be combined. Instead of registering all the point clouds together, the approach only combines the point clouds that contain less repetitive geometric information, which effectively reduces the file size of the final dataset and increases the usability of the data. Base on our validation experiment, the approach represented in this paper is capable of retrieve more geometric information (i.e., higher coverage ratio) using the least number of point clouds (i.e., less file size). Hence, our approach allows construction professionals to rapidly evaluate the information contained in progressive point clouds and selectively combine progressive captured point clouds to provide a complete set of geometric information with reduced file sizes for the BIM update.