16,628 research outputs found
Large Scale SfM with the Distributed Camera Model
We introduce the distributed camera model, a novel model for
Structure-from-Motion (SfM). This model describes image observations in terms
of light rays with ray origins and directions rather than pixels. As such, the
proposed model is capable of describing a single camera or multiple cameras
simultaneously as the collection of all light rays observed. We show how the
distributed camera model is a generalization of the standard camera model and
describe a general formulation and solution to the absolute camera pose problem
that works for standard or distributed cameras. The proposed method computes a
solution that is up to 8 times more efficient and robust to rotation
singularities in comparison with gDLS. Finally, this method is used in an novel
large-scale incremental SfM pipeline where distributed cameras are accurately
and robustly merged together. This pipeline is a direct generalization of
traditional incremental SfM; however, instead of incrementally adding one
camera at a time to grow the reconstruction the reconstruction is grown by
adding a distributed camera. Our pipeline produces highly accurate
reconstructions efficiently by avoiding the need for many bundle adjustment
iterations and is capable of computing a 3D model of Rome from over 15,000
images in just 22 minutes.Comment: Published at 2016 3DV Conferenc
3D Object Reconstruction from Imperfect Depth Data Using Extended YOLOv3 Network
State-of-the-art intelligent versatile applications provoke the usage of full 3D, depth-based streams, especially in the scenarios of intelligent remote control and communications, where virtual and augmented reality will soon become outdated and are forecasted to be replaced by point cloud streams providing explorable 3D environments of communication and industrial data. One of the most novel approaches employed in modern object reconstruction methods is to use a priori knowledge of the objects that are being reconstructed. Our approach is different as we strive to reconstruct a 3D object within much more difficult scenarios of limited data availability. Data stream is often limited by insufficient depth camera coverage and, as a result, the objects are occluded and data is lost. Our proposed hybrid artificial neural network modifications have improved the reconstruction results by 8.53 which allows us for much more precise filling of occluded object sides and reduction of noise during the process. Furthermore, the addition of object segmentation masks and the individual object instance classification is a leap forward towards a general-purpose scene reconstruction as opposed to a single object reconstruction task due to the ability to mask out overlapping object instances and using only masked object area in the reconstruction process
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