Chinese Name:陳奕麟
English Name:Yi-Ling Chen
Grades: PhD Students


Email:yilin (at)


Group in CVLAB: 3D group
Interested in:Shape modeling, surface reconstruction, geometric processing and computing etc.
Thesis:Orientation Inference and Binary Orientation Trees for Surface Reconstruction from Unorganized Point
Orientation information plays an essential role for the success of many geometric modeling and processing algorithms. Despite of the development of many existing algorithms, it remains challenging to reconstruct 3D object surfaces from unoriented sample points. In this thesis, we propose a cooperative algorithm called orientation inference and a space partitioning structure called binary orientation trees (BOTs), which can be easily integrated into a typical surface reconstruction pipeline to enable the existing algorithms to deal with unoriented data sets.

The orientation inference framework starts from building a surface approximation hierarchy comprising of a set of unoriented local surfaces, which are represented as a weighted combination of radial basis functions. We formulate the determination of the globally consistent orientation as a graph optimization problem by treating the local implicit patches as nodes. An energy function is defined to penalize inconsistent orientation changes by checking the sign consistency between neighboring local surfaces. An optimal labeling of the graph nodes indicating the orientation of each local surface can thus be obtained by minimizing the total energy defined on the graph. The local inference results are propagated over the model in a front-propagation fashion to obtain the global solution. The reconstructed surfaces are consolidated by a simple and effective inspection procedure to locate the erroneously fitted local surfaces. A progressive reconstruction algorithm that iteratively includes more oriented points to improve the fitting accuracy and efficiently updates the RBF coefficients is proposed.

Given a complete unoriented point set, we propose the binary orientation tree (BOT) for volume and surface representation, which roughly splits the space into the interior and exterior regions with respect to the input point set. The BOTs are constructed by performing a traditional octree subdivision technique while the corners of each cell are associated with a tag indicating the in/out relationship with respect to the input point set. Starting from the root cell, a growing stage is performed to efficiently assign tags to the connected empty sub-cells. The unresolved tags of the remaining cell corners are determined by examining their visibility via the hidden point removal operator. We show that the outliers accompanying the input point set can be effectively detected during the construction of the BOTs. After removing the outliers and resolving the in/out tags, the BOTs are ready to support any volume or surface representation techniques. To represent the surfaces, we also present a modified MPU implicits algorithm enabled to reconstruct surfaces from the input unoriented point clouds by taking advantage of the BOTs.