Search Results for author:
Found 15 papers, 6 papers with code
PointCNN: Convolution On $\mathcal{X}$-Transformed Points
The proposed method is a generalization of typical CNNs to feature learning from point clouds, thus we call it PointCNN.
Ranked #1 on
3D Instance Segmentation
on S3DIS
(mIoU metric)
PointCNN: Convolution On X-Transformed Points
We present a simple and general framework for feature learning from point cloud.
Ranked #2 on
Semantic Segmentation
on S3DIS Area5
(Number of params metric)
Deep Reinforcement Learning for Producing Furniture Layout in Indoor Scenes
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.
Mutual Information Maximization in Graph Neural Networks
In order to reduce the loss, we extend the GNNs frameworks by exploring the aggregation and iteration scheme in the methodology of mutual information.
Ranked #1 on
Graph Classification
on Citeseer
Multi-Agent Reinforcement Learning of 3D Furniture Layout Simulation in Indoor Graphics Scenes
In the industrial interior design process, professional designers plan the furniture layout to achieve a satisfactory 3D design for selling.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
End-to-end Generative Floor-plan and Layout with Attributes and Relation Graph
We conduct our experiments on the proposed real-world interior layout dataset that contains $191208$ designs from the professional designers.
3D Reconstruction of Simple Objects from A Single View Silhouette Image
While recent deep neural networks have achieved promising results for 3D reconstruction from a single-view image, these rely on the availability of RGB textures in images and extra information as supervision.
Ambient Hidden Space of Generative Adversarial Networks
In this paper, we extend the ambient module to the hidden space of the generator, and provide the uniqueness condition and the corresponding strategy for the ambient hidden generator in the adversarial training process.
Towards Adversarial Training with Moderate Performance Improvement for Neural Network Classification
It has been demonstrated that deep neural networks are prone to noisy examples particular adversarial samples during inference process.
The Direction-Aware, Learnable, Additive Kernels and the Adversarial Network for Deep Floor Plan Recognition
Besides, an adversarial network with two discriminators is proposed to further improve the accuracy of the elements and to reduce the noise of the semantic segmentation.
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.
Structural Plan of Indoor Scenes with Personalized Preferences
In this paper, we propose an assistive model that supports professional interior designers to produce industrial interior decoration solutions and to meet the personalized preferences of the property owners.
Deep Layout of Custom-size Furniture through Multiple-domain Learning
In this paper, we propose a multiple-domain model for producing a custom-size furniture layout in the interior scene.
LWA-HAND: Lightweight Attention Hand for Interacting Hand Reconstruction
The first module is a lightweight feature attention module that extracts both local occlusion representation and global image patch representation in a coarse-to-fine manner.
Hierarchical Reinforcement Learning for Furniture Layout in Virtual Indoor Scenes
In this paper, we explore the furniture layout task as a Markov decision process (MDP) in virtual reality, which is solved by hierarchical reinforcement learning (HRL).
Hierarchical Reinforcement Learning
reinforcement-learning
+1
