  Depth imaging has largely focused on sensor and intrinsics properties.
However, the accuracy of acquire pixel is largely dependent on the capture. We
propose a new depth estimation and approximation algorithm which takes an
arbitrary 3D point cloud as input, with potentially complex geometric
structures, and generates automatically a bounding box which is used to clamp
the 3D distribution into a valid range. We then infer the desired compact
geometric network from complex 3D geometries by using a series of adaptive
joint bilateral filters. Our approach leverages these input depth in the
construction of a compact descriptive adaptive filter framework. The built
system that allows a user to control the result of capture depth map to fit the
target geometry. In addition, it is desirable to visualize structurally
problematic areas of the depth data in a dynamic environment. To provide this
feature, we investigate a fast update algorithm for the fragility of each
pixel's corresponding 3D point using machine learning. We present a new for of
feature vector analysis and demonstrate the effectiveness in the dataset. In
our experiment, we demonstrate the practicality and benefits of our proposed
method by computing accurate solutions captured depth map from different types
of sensors and shows better results than existing methods.
