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Interaction with the environment is a key factor affecting the development of plants and plant ecosystems. In this paper we introduce a modeling framework that makes it possible to simulate and visualize a wide range of interactions at the level of plant architecture. This framework extends the formalism of Lindenmayer systems with constructs needed to(More)
Modeling and rendering of natural scenes with thousands of plants poses a number of problems. The terrain must be modeled and plants must be distributed throughout it in a realistic manner, reflecting the interactions of plants with each other and with their environment. Geometric models of individual plants, consistent with their positions within the(More)
The use of 3D printing has rapidly expanded in the past couple of years. It is now possible to produce 3D-printed objects with exceptionally high fidelity and precision. However, although the quality of 3D printing has improved, both the time to print and the material costs have remained high. Moreover, there is no guarantee that a printed model is(More)
Procedural representations provide powerful means for generating complex geometric structures. They are also notoriously difficult to control. In this article, we present an algorithm for controlling grammar-based procedural models. Given a grammar and a high-level specification of the desired production, the algorithm computes a production from the grammar(More)
We present a method for generating realistic models of temperate-climate trees and shrubs. This method is based on the biological hypothesis that the form of a developing tree emerges from a self-organizing process dominated by the competition of buds and branches for light or space, and regulated by internal signaling mechanisms. Simulations of this(More)
Real-world applications could benefit from the ability to automatically generate a fine-grained ranking of photo aesthetics. However, previous methods for image aesthetics analysis have primarily focused on the coarse, binary categorization of images into high-or low-aesthetic categories. In this work, we propose to learn a deep convolutional neural network(More)
This paper investigates problems of image style, aesthetics , and quality estimation, which require fine-grained details from high-resolution images, utilizing deep neural network training approach. Existing deep convolutional neural networks mostly extracted one patch such as a down-sized crop from each image as a training example. However, one patch may(More)
Input SO AMC HS SIA HC FT Ours GT Figure 1: Sample saliency maps of several state-of-the-art methods (SO [39], AMC [15], HS [34] and SIA [6]) and methods with fast speed (HC [5], FT [1] and ours). Our method runs at about 80 FPS using a single thread, and produces saliency maps of high quality. Previous methods with similar speed, like HC and FT, usually(More)