Abstract
The paper presents an automatic region detection based method to reconstruct street scenes from driving recorder images. The driving recorder in this paper is a dashboard camera that collects images while the motor vehicle is moving. An enormous number of moving vehicles are included in the collected data because the typical recorders are often mounted in the front of moving vehicles and face the forward direction, which can make matching points on vehicles and guardrails unreliable. Believing that utilizing these image data can reduce street scene reconstruction and updating costs because of their low price, wide use, and extensive shooting coverage, we therefore proposed a new method, which is called the Mask automatic detecting method, to improve the structure results from the motion reconstruction. Note that we define vehicle and guardrail regions as “mask” in this paper since the features on them should be masked out to avoid poor matches. After removing the feature points in our new method, the camera poses and sparse 3D points that are reconstructed with the remaining matches. Our contrast experiments with the typical pipeline of structure from motion (SfM) reconstruction methods, such as Photosynth and VisualSFM, demonstrated that the Mask decreased the root-mean-square error (RMSE) of the pairwise matching results, which led to more accurate recovering results from the camera-relative poses. Removing features from the Mask also increased the accuracy of point clouds by nearly 30%–40% and corrected the problems of the typical methods on repeatedly reconstructing several buildings when there was only one target building.
Highlights
Due to the increasing popularity of using reconstruction technologies, more 3D supports are needed.Researchers have proposed many methods to generate 3D models
This paper proposed a method to reconstruct street scenes with data from driving recorders, which are widely used in private and public vehicles
This low-cost method will be beneficial to reducing the cost and shortening the update time required for street scene reconstruction
Summary
Due to the increasing popularity of using reconstruction technologies, more 3D supports are needed.Researchers have proposed many methods to generate 3D models. Light Detection and Ranging (LIDAR) data, and video aerial image sequences have been used to build models combined [2]. These methods can reconstruct the model of large-area cities at a high efficiency; the models reconstructed from aerial data always have lacked detailed information, which constrains their further applications. In order to reconstruct city models with rich details, the terrestrial data based reconstruction has been explored [3,4,5]; and street scenes have been reconstructed with imagery taken from different view angles [3,4] These images were captured by a moving vehicle that carried a GPS/INS navigation system. The integrated GPS/Inertial Navigation Systems (INS) navigation system and mobile
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