Scene Parsing
75 papers with code • 2 benchmarks • 4 datasets
Scene parsing is to segment and parse an image into different image regions associated with semantic categories, such as sky, road, person, and bed. MIT Description
Benchmarks
These leaderboards are used to track progress in Scene Parsing
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Libraries
Use these libraries to find Scene Parsing models and implementations
Cityscapes
Stanford Background
PGDP5K
UNDD
Subtasks
Latest papers
OneFormer: One Transformer to Rule Universal Image Segmentation
huggingface/transformers
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CVPR 2023
However, such panoptic architectures do not truly unify image segmentation because they need to be trained individually on the semantic, instance, or panoptic segmentation to achieve the best performance.
VIBUS: Data-efficient 3D Scene Parsing with VIewpoint Bottleneck and Uncertainty-Spectrum Modeling
In the first stage, we perform self-supervised representation learning on unlabeled points with the proposed Viewpoint Bottleneck loss function.
Boosting Night-time Scene Parsing with Learnable Frequency
wangsen99/FDLNet
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Based on this, we propose to exploit the image frequency distributions for night-time scene parsing.
A Dense Material Segmentation Dataset for Indoor and Outdoor Scene Parsing
A key algorithm for understanding the world is material segmentation, which assigns a label (metal, glass, etc.)
Plane Geometry Diagram Parsing
Geometry diagram parsing plays a key role in geometry problem solving, wherein the primitive extraction and relation parsing remain challenging due to the complex layout and between-primitive relationship.
FLOAT: Factorized Learning of Object Attributes for Improved Multi-object Multi-part Scene Parsing
Our framework involves independent dense prediction of object category and part attributes which increases scalability and reduces task complexity compared to the monolithic label space counterpart.
TO-Scene: A Large-scale Dataset for Understanding 3D Tabletop Scenes
Experiments show that the algorithms trained on TO-Scene indeed work on the realistic test data, and our proposed tabletop-aware learning strategy greatly improves the state-of-the-art results on both 3D semantic segmentation and object detection tasks.
Edge-aware Guidance Fusion Network for RGB Thermal Scene Parsing
Considering the importance of high level semantic information, we propose a global information module and a semantic information module to extract rich semantic information from the high-level features.
Mesh Convolution with Continuous Filters for 3D Surface Parsing
In this paper, we propose a series of modular operations for effective geometric feature learning from 3D triangle meshes.
Pointly-supervised 3D Scene Parsing with Viewpoint Bottleneck
open-air-sun/viewpoint-bottleneck
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Semantic understanding of 3D point clouds is important for various robotics applications.
