Transition Dipole, Charge Transfer, and Electron-hole Coherence in Two-photon Absorption： Visualizations with Two Dimensional Site and Three Dimensional Cube Representations

The developed visualization methods of two dimensional （2D） site and three dimensional （3D） cube representations have been performed to show the orientation of transition dipole, charge transfer, and electron-hole coherence in two-photon absorption （TPA）. The 3D cube representations of transition density can reveal visually the orientation and strength of transition dipole moment, and charge different density show the orientation of charge transfer in TPA. The 2D site representation can reveal visually the electron-hole coherence in TPA. The combination of 2D site and 3D cube representations provide clearly inspect into the charge transfer process and the contribution of excited molecular segments for TPA.

A ubiquitous knowledgeable data representation model(UKRM) for three-dimensional geographic information systems(3D GIS)

In the face of complicated, diversified three-dimensional world, the existing 3D GIS data models suffer from certain issues such as data incompatibility, insufficiency in data representation and representation types, among others. It is often hard to meet the requirements of multiple application purposes(users) related to GIS spatial data management and data query and analysis, especially in the case of massive spatial objects. In this study, according to the habits of human thinking and recognition, discrete expressions(such as discrete curved surface(DCS), and discrete body(DB)) were integrated and two novel representation types(including function structure and mapping structure) were put forward. A flexible and extensible ubiquitous knowledgeable data representation model(UKRM) was then constructed, in which structurally heterogeneous multiple expressions(including boundary representation(B-rep), constructive solid geometry(CSG), functional/parameter representation, etc.) were normalized. GIS's ability in representing the massive, complicated and diversified 3D world was thus greatly enhanced. In addition, data reuse was realized, and the bridge linking static GIS to dynamic GIS was built up. Primary experimental results illustrated that UKRM was overwhelmingly superior to the current data models(e.g. IFC, City GML) in describing both regular and irregular spatial objects.

A survey of deep learning-based 3D shape generation

Deep learning has been successfully used for tasks in the 2D image domain.Research on 3D computer vision and deep geometry learning has also attracted attention.Considerable achievements have been made regarding feature extraction and discrimination of 3D shapes.Following recent advances in deep generative models such as generative adversarial networks,effective generation of 3D shapes has become an active research topic.Unlike 2D images with a regular grid structure,3D shapes have various representations,such as voxels,point clouds,meshes,and implicit functions.For deep learning of 3D shapes,shape representation has to be taken into account as there is no unified representation that can cover all tasks well.Factors such as the representativeness of geometry and topology often largely affect the quality of the generated 3D shapes.In this survey,we comprehensively review works on deep-learning-based 3D shape generation by classifying and discussing them in terms of the underlying shape representation and the architecture of the shape generator.The advantages and disadvantages of each class are further analyzed.We also consider the 3D shape datasets commonly used for shape generation.Finally,we present several potential research directions that hopefully can inspire future works on this topic.

Neural 3D reconstruction from sparse views using geometric priors

Sparse view 3D reconstruction has attracted increasing attention with the development of neural implicit 3D representation.Existing methods usually only make use of 2D views,requiring a dense set of input views for accurate 3D reconstruction.In this paper,we show that accurate 3D reconstruction can be achieved by incorporating geometric priors into neural implicit 3D reconstruction.Our method adopts the signed distance function as the 3D representation,and learns a generalizable 3D surface reconstruction model from sparse views.Specifically,we build a more effective and sparse feature volume from the input views by using corresponding depth maps,which can be provided by depth sensors or directly predicted from the input views.We recover better geometric details by imposing both depth and surface normal constraints in addition to the color loss when training the neural implicit 3D representation.Experiments demonstrate that our method both outperforms state-of-the-art approaches,and achieves good generalizability.

A survey on deep geometry learning:From a representation perspective

Researchers have achieved great success in dealing with 2 D images using deep learning.In recent years,3 D computer vision and geometry deep learning have gained ever more attention.Many advanced techniques for 3 D shapes have been proposed for different applications.Unlike 2 D images,which can be uniformly represented by a regular grid of pixels,3 D shapes have various representations,such as depth images,multi-view images,voxels,point clouds,meshes,implicit surfaces,etc.The performance achieved in different applications largely depends on the representation used,and there is no unique representation that works well for all applications.Therefore,in this survey,we review recent developments in deep learning for 3 D geometry from a representation perspective,summarizing the advantages and disadvantages of different representations for different applications.We also present existing datasets in these representations and further discuss future research directions.

Parallel algorithm for viewshed analysis on a modern GPU

Spatial analysis,including viewshed analysis,is an important aspect of the Digital Earth system.Viewshed analysis is usually performed on a large scale,so efficiency is important in any Digital Earth application making these calculations.In this paper,a real-time algorithm for viewshed analysis in 3D scenes is presented by using the parallel computing capabilities of a graphics processing unit(GPU).In contrast to traditional algorithms based on line-of-sight,this algorithm runs completely within the programmable 3D visualization pipeline to render 3D terrains with viewshed analysis.The most important difference is its integration of the viewshed calculation with the rendering module.Invisible areas are rendered as shadows in the 3D scene.The algorithm process is paralleled by rasterizer units in the graphics card and by vertex and pixel shaders executed on the GPU.We have implemented this method in our 3D Digital Earth system with the DirectX 9.0c API and tested on some consumer-level PC platforms with interactive framerates and high image quality.Our algorithm has been widely used in related systems based on Digital Earth.

Toward optimizing the design of virtual environments for route learning:empirically assessing the effects of changing levels of realism on memory

Broadly,this paper is about designing memorable 3D geovisualizations for spatial knowledge acquisition during(virtual)navigation.Navigation is a fundamentally important task,and even though most people navigate every day,many find it difficult in unfamiliar environments.When people get lost in an unfamiliar environment,or are unable to remember a route that they took,they might feel anxiety,disappointment and frustration;and in real world,such incidents can be costly,and at times,life-threatening.Therefore,in this paper,we study the design decisions in terms of visual realism in a city model,propose a visualization design optimized for route learning,implement and empirically evaluate this design.The evaluation features a navigational route learning task,where we measure shortand long-term recall accuracy of 42 participants with varying spatial abilities and memory capacity.Our findings provide unique empirical evidence on how design choices affect memory in route learning with geovirtual environments,contributing toward empirically verified design guidelines for digital cities.