Adversarial Model for Rotated Indoor Scenes Planning
In this paper, we propose an adversarial model for producing furniture layout for interior scene synthesis when the interior room is rotated. The proposed model combines a conditional adversarial network, a rotation module, a mode module, and a rotation discriminator module. As compared with the prior work on scene synthesis, our proposed three modules enhance the ability of auto-layout generation and reduce the mode collapse during the rotation of the interior room. We conduct our experiments on a proposed real-world interior layout dataset that contains 14400 designs from the professional designers. Our numerical results demonstrate that the proposed model yields higher-quality layouts for four types of rooms, including the bedroom, the bathroom, the study room, and the tatami room.
Code (0)
등록된 구현이 없습니다.
Tasks
Layout GenerationmodelSimilar Papers 제목 키워드 기반
Self-Supervised Monocular Depth Estimation of Untextured Indoor Rotated Scenes
Self-supervised deep learning methods have leveraged stereo images for training monocular depth estimation. Although these methods show strong results on outdoor datasets such as KITTI, they do not match performance of s…
Depth EstimationImage ReconstructionMonocular Depth EstimationSelf-Supervised LearningIndoor Scene Generation from a Collection of Semantic-Segmented Depth Images
We present a method for creating 3D indoor scenes with a generative model learned from a collection of semantic-segmented depth images captured from different unknown scenes. Given a room with a specified size, our metho…
Generative Adversarial NetworkScene GenerationPath Planning in Support of Smart Mobility Applications using Generative Adversarial Networks
This paper describes and evaluates the use of Generative Adversarial Networks (GANs) for path planning in support of smart mobility applications such as indoor and outdoor navigation applications, individualized wayfindi…
Autonomous VehiclesNavigateAdversarial learning for unguided single depth map completion of indoor scenes
Depth map completion without guidance from color images is a challenging, ill-posed problem. Conventional methods rely on computationally intensive optimization processes. This work proposes a deep adversarial learning a…
3D-Aware Indoor Scene Synthesis with Depth Priors
Despite the recent advancement of Generative Adversarial Networks (GANs) in learning 3D-aware image synthesis from 2D data, existing methods fail to model indoor scenes due to the large diversity of room layouts and the …
3D-Aware Image Synthesis3D geometryDiversityImage Generation+1