Table of Content
This paper presents a fully-automatic system for generating an optimized indoor scene populated by a variety of furniture objects. Given positive examples of furnished indoor scenes, our system extracts hierarchical and spatial relationships for different types of furniture objects. The extracted relationships are encoded into priors which are integrated into a cost function that optimizes ergonomic factors, such as visibility and accessibility. To deal with the prohibitively large search space, the cost function is optimized by simulated annealing with a Metropolis Hastings state-search step. A perceptual study is performed to validate that there is no significant difference in preference on functionality between our synthesized results and those produced by human designers.
A method for automated generation of building layouts for computer graphics applications using a Bayesian network trained on real-world data to construct a complete three-dimensional building with internal structure. A heuristic approach in developing a cost function by adding some new cost functions to the previous method that can suggest the furniture layout for multiple rooms in the new house is proposed. This work proposes a system using a procedural approach based on L-systems to model cities with methods that allow the consideration of global goals and local constraints and reduce the complexity of the production rules. The Magic Decorator system is presented, a system that automatically generates material suggestions for 3D indoor scenes and can automatically and efficiently produce a series of visually plausible material suggestions which are comparable to those produced by artists.
Make it Home: Automatic Optimization of Furniture Arrangement
This work draws from the literature on design and human cognition to better understand the design processes of novice and casual modelers, whose goals and motivations are often distinct from those of professional artists.
This paper introduces a framework for the automatic generation of furniture layouts, avoiding manual or semi-automated layout approaches, which are impractical in many graphics applications requiring full automation. We believe that our work is the first to comprehensively consider human factors such as accessibility, visibility, pathway constraints, and so forth. In addition, our results have been deemed by human observers to be perceptually valid in functionality when compared with real arrangements generated by human designers. This paper presents a procedural approach to generate furniture arrangements for large virtual indoor scenes, to only furnish the rooms in the vicinity of the viewer while the user explores a building in real time. A novel method for fast generation of furniture arrangements in interior scenes that achieves comparable results to a recent method for automatic interior design in terms of user preferences and that local procedural design enhances the result of optimization-based interior design.
Procedural modeling of buildings
This work presents a general-purposed constraint-based system able to process a complex set of constraints, including geometric and pseudo-physics ones, and universal quantifiers and Boolean combination of constraints are allowed to make the layout description easier. A system that automatically populates indoor virtual scenes with furniture objects and optimizes their positions and orientations with respect to aesthetic, ergonomic and functional rules called interior design guidelines is presented. This paper proposes an Augmented Reality interior design tool that the furniture reseller can make virtual 3D furniture available, and then via AR the virtual furniture are rendered in the real scene of the home space.
An interactive example-based system for non-expert users to generate 3D indoor scenes intuitively and measurably increases the visual quality and time efficiency compared with the completely manual scene editing or modeling. This research focuses on human perception of 3D solid textures composed of aggregate particles in a binding matrix and considers four methods for approximating plausible particle shapes—including two methods of the author's own contribution. A semi-automatic image-based approach to façade modeling that uses images captured along streets and relies on structure from motion to recover camera positions and point clouds automatically as the initial stage for modeling is proposed. CGA shape is shown to efficiently generate massive urban models with unprecedented level of detail, with the virtual rebuilding of the archaeological site of Pompeii as a case in point.
What Is Semantic Scholar?
This work proposes a method to automatically generate furniture layouts for indoor environments using a general mechanism -- rejection sampling -- which provides great flexibility at the cost of extra computation, and applies a wide variety of constraints relevant to real-world applications. While in recent years numerous publications have appeared demonstrating the automatic modeling of building exteriors and facades, the automatic generation of realistic indoor configurations has not yet received the attention that it deserves. Currently, such indoor modeling is usually simplified or even ignored, which severely limits the realism of many virtual environments. This work presents a general-purposed constraint-based system that is able to process a complex set of constraints, including geometric and pseudo-physics ones, and universal quantifiers and Boolean combination of constraints are allowed to make the layout description easier.
No comments:
Post a Comment