Learning a probabilistic latent space of object shapes via 3D generative-adversarial modeling

We study the problem of 3D object generation. We propose a novel framework, namely 3D Generative Adversarial Network (3D-GAN), which generates 3D objects from a probabilistic space by leveraging recent advances in volumetric convo-lutional networks and generative adversarial nets. The benefits of our model are three-fold: first, the use of an adversarial criterion, instead of traditional heuristic criteria, enables the generator to capture object structure implicitly and to synthesize high-quality 3D objects; second, the generator establishes a mapping from a low-dimensional probabilistic space to the space of 3D objects, so that we can sample objects without a reference image or CAD models, and explore the 3D object manifold; third, the adversarial discriminator provides a powerful 3D shape descriptor which, learned without supervision, has wide applications in 3D object recognition. Experiments demonstrate that our method generates high-quality 3D objects, and our unsupervisedly learned features achieve impressive performance on 3D object recognition, comparable with those of supervised learning methods

Improved clustering approach for junction detection of multiple edges with modified freeman chain code

Image processing framework of two-dimensional line drawing involves three phases that are detecting junction and corner that exist in the drawing, representing the lines, and extracting features to be used in recognizing the line drawing based on the representation scheme used. As an alternative to the existing frameworks, this thesis proposed a framework that consists of improvement in the clustering approach for junction detection of multiple edges, modified Freeman chain code scheme and provide new features and its extraction, and recognition algorithm. This thesis concerns with problem in clustering line drawing for junction detection of multiple edges in the first phase. Major problems in cluster analysis such as time taken and particularly number of accurate clusters contained in the line drawing when performing junction detection are crucial to be addressed. Two clustering approaches are used to compare with the result obtained from the proposed algorithm: self-organising map (SOM) and affinity propagation (AP). These approaches are chosen based on their similarity as unsupervised learning class and do not require initial cluster count to execute. In the second phase, a new chain code scheme is proposed to be used in representing the direction of lines and it consists of series of directional codes and corner labels found in the drawing. In the third phase, namely feature extraction algorithm, three features proposed are length of lines, angle of corners, and number of branches at each corner. These features are then used in the proposed recognition algorithm to match the line drawing, involving only mean and variance in the calculation. Comparison with SOM and AP clustering approaches resulting in up to 31% reduction for cluster count and 57 times faster. The results on corner detection algorithm shows that it is capable to detect junction and corner of the given thinned binary image by producing a new thinned binary image containing markers at their locations

Line Primitives and Their Applications in Geometric Computer Vision

Line primitives are widely found in structured scenes which provide a higher level of structure information about the scenes than point primitives. Furthermore, line primitives in space are closely related to Euclidean transformations, because the dual vector (also known as Pluecker coordinates) representation of 3D lines is the counterpart of the dual quaternion which depicts an Euclidean transformation. These geometric properties of line primitives motivate the work in this thesis with the following contributions: Firstly, by combining local appearances of lines and geometric constraints between line pairs in images, a line segment matching algorithm is developed which constructs a novel line band descriptor to depict the local appearance of a line and builds a relational graph to measure the pair-wise consistency between line correspondences. Experiments show that the matching algorithm is robust to various image transformations and more efficient than conventional graph based line matching algorithms. Secondly, by investigating the symmetric property of line directions in space, this thesis presents a complete analysis about the solutions of the Perspective-3-Line (P3L) problem which estimates the camera pose from three reference lines in space and their 2D projections. For three spatial lines in general configurations, a P3L polynomial is derived which is employed to develop a solution of the Perspective-n-Line problem. The proposed robust PnL algorithm can efficiently and accurately estimate the camera pose for both small numbers and large numbers of line correspondences. For three spatial lines in special configurations (e.g., in a Manhattan world which consists of three mutually orthogonal dominant directions), the solution of the P3L problem is employed to solve the vanishing point estimation and line classification problem. The proposed vanishing point estimation algorithm achieves high accuracy and efficiency by thoroughly utilizing the Manhattan world characteristic. Another advantage of the proposed framework is that it can be easily generalized to images taken by central catadioptric cameras or uncalibrated cameras. The third major contribution of this thesis is about structure-from-motion using line primitives. To circumvent the Pluecker constraints on the Pluecker coordinates of lines, the Cayley representation of lines is developed which is inspired by the geometric property of the Pluecker coordinates of lines. To build the line observation model, two derivations of line projection functions are presented: one is based on the dual relationship between points and lines; and the other is based on the relationship between Pluecker coordinates and the Pluecker matrix. Then the motion and structure parameters are initialized by an incremental approach and optimized by sparse bundle adjustment. Quantitative validations show the increase in performance when compared to conventional line reconstruction algorithms