Scene Labeling
4 papers with code • 0 benchmarks • 1 datasets
Benchmarks
These leaderboards are used to track progress in Scene Labeling
Stanford Background
Latest papers with no code
Towards Panoptic 3D Parsing for Single Image in the Wild
Our proposed panoptic 3D parsing framework points to a promising direction in computer vision.
Hierarchical Point-Edge Interaction Network for Point Cloud Semantic Segmentation
To incorporate point features in the edge branch, we establish a hierarchical graph framework, where the graph is initialized from a coarse layer and gradually enriched along the point decoding process.
End-to-End 3D-PointCloud Semantic Segmentation for Autonomous Driving
Imbalanced distribution of classes in the dataset is one of the challenges that face 3D semantic scene labeling task.
A Joint Convolutional Neural Networks and Context Transfer for Street Scenes Labeling
Our contributions are threefold: (1) A priori s-CNNs model that learns priori location information at superpixel level is proposed to describe various objects discriminatingly; (2) A hierarchical data augmentation method is presented to alleviate dataset bias in the priori s-CNNs training stage, which improves foreground objects labeling significantly; (3) A soft restricted MRF energy function is defined to improve the priori s-CNNs model's labeling performance and reduce the over smoothness at the same time.
RGBD Based Dimensional Decomposition Residual Network for 3D Semantic Scene Completion
RGB images differentiate from depth images as they carry more details about the color and texture information, which can be utilized as a vital complementary to depth for boosting the performance of 3D semantic scene completion (SSC).
Scene Parsing via Dense Recurrent Neural Networks with Attentional Selection
Recurrent neural networks (RNNs) have shown the ability to improve scene parsing through capturing long-range dependencies among image units.
Combining Multi-level Contexts of Superpixel using Convolutional Neural Networks to perform Natural Scene Labeling
Modern deep learning algorithms have triggered various image segmentation approaches.
Multimodal Recurrent Neural Networks with Information Transfer Layers for Indoor Scene Labeling
This paper proposes a new method called Multimodal RNNs for RGB-D scene semantic segmentation.
Dense Recurrent Neural Networks for Scene Labeling
Recently recurrent neural networks (RNNs) have demonstrated the ability to improve scene labeling through capturing long-range dependencies among image units.
Guided Perturbations: Self-Corrective Behavior in Convolutional Neural Networks
Convolutional Neural Networks have been a subject of great importance over the past decade and great strides have been made in their utility for producing state of the art performance in many computer vision problems.
