Perceptual image encryption serves as a pivotal mechanism for delegating processing while ensuring the visual security of image data. The robustness of such encryption schemes is traditionally evaluated through cryptanalysis techniques, yet these approaches heavily rely on manual labor and prerequisite knowledge of the encryption algorithms. Recently, some works attempt to reveal visual content from perceptually encrypted images Based on CNN architectures. However, it is still tricky for these increasingly complex methods to reveal informative visual details. In this study, we focus on the extraction and utilization of inherent hierarchical features within the input image itself to significantly advances the field. To achieve it, we present a novel Progressive Fusion Attack Network (PFAN) to fully explore the hierarchical features. PFAN incorporates multiple subbranches, forming a progressive fusion structure that facilitates informative hierarchical feature representations and offers robust model fault tolerance. To enhance the reconstruction of encryption-induced distortions, we incorporate a Multiscale Feature Extraction Module (MFEM) that captures robust hierarchical features across various scales. Meanwhile, a Hierarchical Feature Fusion Module (HFFM) is designed to adaptively integrate and highlight the optimal feature representations, further optimizing the visual content reconstruction process. Extensive experimental evaluation demonstrates that PFAN exhibits remarkable agnosticism towards different perceptual encryption schemes and encryption strengths, achieving superior performance. Furthermore, PFAN outperforms state-of-the-art CNN-based image restoration methods in terms of effectiveness and generalizability.

Urban building reconstruction is an important step for urban digitization and realisticvisualization. In this paper, we propose a novel automatic method to recover urban building geometry from 3D point clouds. The proposed method is suitable for buildings composed of planar polygons and aligned with the gravity direction, which are quite common in the city. Our key observation is that the building shapes are usually piecewise constant along the gravity direction and determined by several dominant shapes. Based on this observation, we formulate building reconstruction as an energy minimization problem under the Markov Random Field (MRF) framework. Specifically, point clouds are first cutinto a sequence of slices along the gravity direction. Then, floorplans are reconstructed by extracting boundaries of these slices, among which dominant floorplans are extracted and propagated to other floors via MRF. To guarantee correct propagation, a new distance measurement for floorplans is designed, which first encodes floorplans into strings and then calculates distances between their corresponding strings. Additionally, an image based editing method is also proposed to recover detailed window structures. Experimental results on both synthetic and real data sets have validated the effectiveness of our method.

Repetition extraction plays an important role in facade image analysis. In this paper, this task is handled within the graph cut based image segmentation framework. To model the repetitions, generalized translation symmetry (GTS) is introduced to enable aperiodic repetition layouts. More importantly, GTS is explicitly formulated in terms of matrix multiplication. That is, GTS is viewed as the product of a repetitive pattern and two block matrices. These two block matrices are employed to represent the vertical and horizontal symmetry respectively. On this basis, repetition extraction is formulated as a GTS constrained energy minimization problem. An alternatively optimization algorithm based on graph cut and dynamic programming is finally developed to solve the problem. Experimental results demonstrate the validity of our method.