Building Footprint Extraction in Dense Areas using Super Resolution and Frame Field Learning

Despite notable results on standard aerial datasets, current state-of-the-arts fail to produce accurate building footprints in dense areas due to challenging properties posed by these areas and limited data availability. In this paper, we propose a framework to address such issues in polygonal building extraction. First, super resolution is employed to enhance the spatial resolution of aerial image, allowing for finer details to be captured. This enhanced imagery serves as input to a multitask learning module, which consists of a segmentation head and a frame field learning head to effectively handle the irregular building structures. Our model is supervised by adaptive loss weighting, enabling extraction of sharp edges and fine-grained polygons which is difficult due to overlapping buildings and low data quality. Extensive experiments on a slum area in India that mimics a dense area demonstrate that our proposed approach significantly outperforms the current state-of-the-art methods by a large margin.Comment: Accepted at The 12th International Conference on Awareness Science and Technolog

Reliable detection and separation of components for solid objects defined with scalar fields

The detection of the number of disjoint components is a well-known procedure for surface objects. However, this problem has not been solved for solid models defined with scalar fields in the so-called implicit form. In this paper, we present a technique which allows for detection of the number of disjoint components with a predefined tolerance for an object defined with a single scalar function. The core of the technique is a reliable continuation of the spatial enumeration based on the interval methods. We also present several methods for separation of components using set-theoretic operations for further handling these components individually in a solid modelling system dealing with objects defined with scalar fields

Object polygonization in traffic scenes using small Eigenvalue analysis

Shape polygonization is an effective and convenient method to compress the storage requirements of a shape curve. Polygonal approximation offers an invariant representation of local properties even after digitization of a shape curve. In this paper, we propose to use universal threshold for polygonal approximation of any two-dimensional object boundary by exploiting the strength of small eigenvalues. We also propose to adapt the Jaccard Index as a metric to measure the effectiveness of shape polygonization. In the context of this paper, we have conducted extensive experiments on the semantically segmented images from Cityscapes dataset to polygonize the objects in the traffic scenes. Further, to corroborate the efficacy of the proposed method, experiments on the MPEG-7 shape database are conducted. Results obtained by the proposed technique are encouraging and can enable greater compression of annotation documents. This is particularly critical in the domain of instrumented vehicles where large volumes of high quality video must be exhaustively annotated without loss of accuracy and least man-hours

Interactive ray shading of FRep objects

In this paper we present a method for interactive rendering general procedurally defined functionally represented (FRep) objects using the acceleration with graphics hardware, namely Graphics Processing Units (GPU). We obtain interactive rates by using GPU acceleration for all computations in rendering algorithm, such as ray-surface intersection, function evaluation and normal computations. We compute primary rays as well as secondary rays for shadows, reflection and refraction for obtaining high quality of the output visualization and further extension to ray-tracing of FRep objects. The algorithm is well-suited for modern GPUs and provides acceptable interactive rates with good quality of the results. A wide range of objects can be rendered including traditional skeletal implicit surfaces, constructive solids, and purely procedural objects such as 3D fractals