This paper reviews the past and current trends of three-dimensional (3D) modeling and reconstruction of plants and trees. These topics have been studied in multiple research fields, including computer vision, graphics, plant phenotyping, and forestry. This paper, therefore, provides a cross-cutting review. Representations of plant shape and structure are first summarized, where every method for plant modeling and reconstruction is based on a shape/structure representation. The methods were then categorized into 1) creating non-existent plants (modeling) and 2) creating models from real-world plants (reconstruction). This paper also discusses the limitations of current methods and possible future directions.

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3D modeling and reconstruction of plants and trees: A cross-cutting review across computer graphics, vision, and plant phenotyping

Enabling users to push the physical world’s limits, augmented and virtual reality platforms opened a new chapter in perception. Novel immersive experiences resulted in the emergence of new interaction methods for virtual environments, which came with unprecedented security and privacy risks. This paper presents a keylogging inference attack to infer user inputs typed with in-air tapping keyboards. We observe that hands follow specific patterns when typing in the air and exploit this observation to carry out our attack. Starting with three plausible attack scenarios where the adversary obtains the hand trace patterns of the victim, we build a pipeline to reconstruct the user input. Our attack pipeline takes the hand traces of the victim as an input and outputs a set of input inferences ordered from the best to worst. Through various experiments, we showed that our inference attack achieves a pinpoint accuracy ranging from 40% to 87% within at most the top-500 candidate reconstructions. Finally, we discuss countermeasures, while the results presented provide a cautionary tale of the security and privacy risk of the immersive mobile technology.

A Keylogging Inference Attack on Air-Tapping Keyboards in Virtual Environments

Enabling users to push the physical world&#x2019;s limits, augmented and virtual reality platforms opened a new chapter in perception. Novel immersive experiences resulted in the emergence of new interaction methods for virtual environments, which came with unprecedented security and privacy risks. This paper presents a keylogging inference attack to infer user inputs typed with in-air tapping keyboards. We observe that hands follow specific patterns when typing in the air and exploit this observation to carry out our attack. Starting with three plausible attack scenarios where the adversary obtains the hand trace patterns of the victim, we build a pipeline to reconstruct the user input. Our attack pipeline takes the hand traces of the victim as an input and outputs a set of input inferences ordered from the best to worst. Through various experiments, we showed that our inference attack achieves a pinpoint accuracy ranging from 40% to 87% within at most the top-500 candidate reconstructions. Finally, we discuss countermeasures, while the results presented provide a cautionary tale of the security and privacy risk of the immersive mobile technology. 

Plant disease visualization simulation belongs to an important research area at the intersection of computer application technology and plant pathology. However, due to the variety of plant diseases and their complex causes, how to achieve realistic, flexible, and universal plant disease simulation is still a problem to be explored in depth. Based on the principles of plant disease prediction, a time-varying generic model of diseases affected by common environmental factors was established, and interactive environmental parameters such as temperature, humidity, and time were set to express the plant disease spread and color change processes through a unified calculation. Using the apparent symptoms as the basis for plant disease classification, simulation algorithms for different symptom types were propose. The composition of disease spots was deconstructed from a computer simulation perspective, and the simulation of plant diseases with symptoms such as discoloration, powdery mildew, ring pattern, rust spot, and scatter was realized based on the combined application of visualization techniques such as image processing, noise optimization and texture synthesis. To verify the effectiveness of the algorithm, a simulation similarity test method based on deep learning was proposed to test the similarity with the recognition accuracy of symptom types, and the overall accuracy reaches 87%. The experimental results showed that the algorithm in this paper can realistically and effectively simulate five common plant disease forms. It provided a useful reference for the popularization of plant disease knowledge and visualization teaching, and also had certain research value and application value in the fields of film and television advertising, games, and entertainment.

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Algorithm for appearance simulation of plant diseases based on symptom classification

Advancements in virtual reality (VR) technology enable us to rethink the way of interactive 3D modeling - intuitively creating 3D content directly in 3D space. However, conventional VR-based modeling is laborious and tedious to generate a detailed 3D model in full manual mode since users need to carefully draw almost the entire surface. In this paper, we present a freehand mid-air sketching system with the aid of deep learning techniques for modeling structured buildings, where the user freely draws a few key strokes in mid-air using his/her fingers to represent the desired shapes and our system automatically interprets the strokes using a deep neural network and generates a detailed building model based on a procedural modeling method. After creating several building blocks one by one, the user can freely move, rotate, and combine the blocks to form a complex building model. We demonstrate the ease of use for novice users, effectiveness, and efficiency of our sketching system, BuildingSketch, by presenting a variety of building models.

BuildingSketch: Freehand Mid-Air Sketching for Building Modeling

2D sketch-based tree modeling cannot guarantee to generate plausible depth values and full 3D tree shapes. With the advent of virtual reality (VR) technologies, 3D sketching enables a new form for 3D tree modeling. However, it is labor-intensive and difficult to create realistically-looking 3D trees with complicated geometry and lots of detailed twigs with a reasonable amount of effort. In this paper, we explore the use of mid-air finger 3D sketching in VR for tree modeling. We present a hybrid approach that integrates freehand 3D sketches with an automatic population of branch geometries. The user only needs to draw a few 3D strokes in mid-air to define the envelope of the foliage (denoted as lobes) and main branches. Our algorithm then automatically generates a full 3D tree model based on these stroke inputs. Additionally, the shape of the 3D tree model can be modified by freely dragging, squeezing, or moving lobes in mid-air. We demonstrate the ease-of-use, efficiency, and flexibility in tree modeling and overall shape control. We perform user studies and show a variety of realistic tree models generated instantaneously from 3D finger sketching.

Fanxing Zhang

Papers

Mid-Air Finger Sketching for Tree Modeling

BuildingSketch: Freehand Mid-Air Sketching for Building Modeling