activity
20182022
most citedThe Direction-Aware, Learnable, Additive Kernels and the Adversarial Network for Deep Floor Plan Recognition

15 citations · 24 across the 8 of their papers we have counts for

collaborators

11 papers

cs.CV2022

Hierarchical Reinforcement Learning for Furniture Layout in Virtual Indoor Scenes

Xinhan Di, Pengqian Yu

In real life, the decoration of 3D indoor scenes through designing furniture layout provides a rich experience for people. In this paper, we explore the furniture layout task as a…

cs.CV20212 cited

Multi-Agent Reinforcement Learning of 3D Furniture Layout Simulation in Indoor Graphics Scenes

Xinhan Di, Pengqian Yu

In the industrial interior design process, professional designers plan the furniture layout to achieve a satisfactory 3D design for selling. In this paper, we explore the interior…

cs.CV20214 cited

Deep Reinforcement Learning for Producing Furniture Layout in Indoor Scenes

Xinhan Di, Pengqian Yu

In the industrial interior design process, professional designers plan the size and position of furniture in a room to achieve a satisfactory design for selling. In this paper, we…

cs.CV20202 cited

End-to-end Generative Floor-plan and Layout with Attributes and Relation Graph

Xinhan Di, Pengqian Yu, Danfeng Yang +3

In this paper, we propose an end-end model for producing furniture layout for interior scene synthesis from the random vector. This proposed model is aimed to support professional…

cs.CV2020

Deep Layout of Custom-size Furniture through Multiple-domain Learning

Xinhan Di, Pengqian Yu, Danfeng Yang +3

In this paper, we propose a multiple-domain model for producing a custom-size furniture layout in the interior scene. This model is aimed to support professional interior designers…

cs.CV20201 cited

Structural Plan of Indoor Scenes with Personalized Preferences

Xinhan Di, Pengqian Yu, Hong Zhu +3

In this paper, we propose an assistive model that supports professional interior designers to produce industrial interior decoration solutions and to meet the personalized preferen…