Concerning the problems that compression and spatial index for point cloud data in 3D model are inefficient and overlapping of two adjacent query windows is a large probability event in the process of roaming, the methods of Adjacent Point Difference Progressive Compression (APDPC) and R-tree spatial index for processing redundants based on trimming overlapped regions were proposed. Firstly, spatial subdivision of 3D model was done by an octree, the point cloud data managed by each leaf node was sorted by means of Morton codes, the data was partitioned according to outer cube size of R-tree leaf node, the data difference between adjacent points in the block was calculated, the difference was progressively compressed by using blocks as units, reading the data blocks in batches to create the R-tree. Secondly, the valid range of this query was calculated with the scope of the last query window. Finally, the query method of point cloud data based on R-tree index was given. This method improved the compression rate of point cloud data by 26.61 percentage points, and could realize streaming transmission. Meanwhile, it effectively reduced I/O overhead, the query performance was improved by 35.44%, and data redundancy was reduced by 16.49 percentage points. The experimental results show that the proposed methods have obvious advantages in 3D virtual travel, digital city and other systems.