Fast Surface Reconstruction Of Depth Image Stream Of Kinect V2 Using Voxel Hashing

The rapid reconstruction of 3D models is a bottleneck that restricts the development of computer graphics and virtual reality technology.The traditional 3D reconstruction technology has problems such as expensive data acquisition equipment,complicated data acquisition process and large amount of data resulting in low efficiency of 3D reconstruction.In recent years,with the development of computer vision and computer graphics and the continuous improvement of performance of related hardware equipment,depth cameras such as Microsoft's Kinect can quickly acquire depth image stream data in the scene at a low cost for video games,medical treatments,real-time 3D reconstruction and other fields,which has a wide range of application value.Therefore,this thesis adopts a low-cost Kinect V2 depth camera to study the fast surface reconstruction method of deep image stream based on voxel hash,and applies the hash function based on MD4 algorithm to fast surface reconstruction to achieve the purpose of real-time storage and update 3D surface data.The thesis mainly completes the following three aspects of work:(1)The construction of real-time data acquisition platform based on Kinect V2 and the acquisition of depth image stream data.Firstly,the Kinect V2 is used to build a data acquisition platform to collect the depth image stream of indoor scene in real time,and the depth image is gradually merged into the 3D model to meet the requirements of high frame rate continuous depth data in the reconstruction process.The experimental results show that in the indoor scene,the error of the depth image acquired by the built-in Kinect V2-based data acquisition platform is almost constant at different distances,and the frame rate of the acquired depth image stream can reach 60 frames per second,the depth data collected can meet the requirements of subsequent research.(2)A hash function based on MD4 algorithm is proposed to improve the efficiency of data access,and the separate-chaining method is used to effectively solve the problem of data collision.The depth data stored in the traditional way to the voxel hash table is prone to collision,resulting in slow data storage and ultimately affecting the reconstruction speed and scale.In the field of cryptography,MD4 algorithm has been widely used and applied due to its high computational efficiency and good anti-collision advantage.It is inspired by MD4 algorithm in these two aspects,this thesis introduces a hash function based on MD4 algorithm to improve the efficiency of data searching,updating,and inserting,and using the method of separate chaining method to solve the data collision problem mapped to the same location.The method solves the collision problem mapped to the same location by inserting a certain number of hash entries in each bucket,if the bucket filled,the data that caused collision is stored by linear probing to find the next available location.The results show that the load rate of hash table can reach 95.12%,and it only takes 1.3 times to find and insert data successfully on the average,which can meet the test requirements.(3)A fast surface reconstruction method based on voxel hash table is studied.First,the obtained depth image stream is preprocessed to obtain the pose of the camera,then a new voxel block is allocated according to the input depth image,and the descriptor of the voxel block is inserted into the hash table,and then in the fusion step,the voxel block allocated in the current field of view is updated,and the acquired new depth image is merged into the currently visible voxel block data and updated.Finally,illuminate to extract the iso-surface containing the associated color through the ray casting method.The thesis mainly studies the design of hash function and hash table in fast surface reconstruction method based on depth image stream data.The experimental results show that the method based on MD4 can quickly achieve the purpose of the surface reconstruction of indoor scene,and the frame rate can reach 25~35 frames per second.Compared with the results of the Microsoft KinectFusion,the details of the reconstruction are finer,and the reconstruction efficiency is improved by about 8% compared with the Voxel Hashing algorithm.Compared with the traditional hash table model,the hash space used in this thesis occupies only about 40 M,which reduces the risk of memory overflow,has good scalability,and can meet the application requirements in the field of automation modeling.