Conventionally, interior lighting design is technically complex yet challenging and requires professional knowledge and aesthetic disciplines of designers. This paper presents a new digital lighting design framework for virtual interior scenes, which allows novice users to automatically obtain lighting layouts and interior rendering images with visually pleasing lighting effects. The proposed framework utilizes neural networks to retrieve and learn underlying design guidelines and the principles beneath the existing lighting designs, e.g., a newly constructed dataset of 6K 3D interior scenes from professional designers with dense annotations of lights. With a 3D furniture-populated indoor scene as the input, the framework takes two stages to perform lighting design: 1) lights are iteratively placed in the room; 2) the colors and intensities of the lights are optimized by an adversarial scheme, resulting in lighting designs with aesthetic lighting effects. Quantitative and qualitative experiments show that the proposed framework effectively learns the guidelines and principles and generates lighting designs that are preferred over the rule-based baseline and comparable to those of professional human designers.

With the rapid development of data‐driven techniques, data has played an essential role in various computer vision tasks. Many realistic and synthetic datasets have been proposed to address different problems. However, there are lots of unresolved challenges: (1) the creation of dataset is usually a tedious process with manual annotations, (2) most datasets are only designed for a single specific task, (3) the modification or randomization of the 3D scene is difficult, and (4) the release of commercial 3D data may encounter copyright issue. This paper presents MINERVAS, a Massive INterior EnviRonments VirtuAl Synthesis system, to facilitate the 3D scene modification and the 2D image synthesis for various vision tasks. In particular, we design a programmable pipeline with Domain‐Specific Language, allowing users to select scenes from the commercial indoor scene database, synthesize scenes for different tasks with customized rules, and render various types of imagery data, such as color images, geometric structures, semantic labels. Our system eases the difficulty of customizing massive scenes for different tasks and relieves users from manipulating fine‐grained scene configurations by providing user‐controllable randomness using multilevel samplers. Most importantly, it empowers users to access commercial scene databases with millions of indoor scenes and protects the copyright of core data assets, e.g., 3D CAD models. We demonstrate the validity and flexibility of our system by using our synthesized data to improve the performance on different kinds of computer vision tasks. The project page is at https://coohom.github.io/MINERVAS.

With the rapid development of data-driven techniques, data has played an essential role in various computer vision tasks. Many realistic and synthetic datasets have been proposed to address different problems. However, there are lots of unresolved challenges: (1) the creation of dataset is usually a tedious process with manual annotations, (2) most datasets are only designed for a single specific task, (3) the modification or randomization of the 3D scene is difficult, and (4) the release of commercial 3D data may encounter copyright issue. This paper presents MINERVAS, a Massive INterior EnviRonments VirtuAl Synthesis system, to facilitate the 3D scene modification and the 2D image synthesis for various vision tasks. In particular, we design a programmable pipeline with Domain-Specific Language, allowing users to (1) select scenes from the commercial indoor scene database, (2) synthesize scenes for different tasks with customized rules, and (3) render various imagery data, such as visual color, geometric structures, semantic label. Our system eases the difficulty of customizing massive numbers of scenes for different tasks and relieves users from manipulating fine-grained scene configurations by providing user-controllable randomness using multi-level samplers. Most importantly, it empowers users to access commercial scene databases with millions of indoor scenes and protects the copyright of core data assets, e.g., 3D CAD models. We demonstrate the validity and flexibility of our system by using our synthesized data to improve the performance on different kinds of computer vision tasks.

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