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
Most implemented papers
Context-Aware Synthesis and Placement of Object Instances
NVlabs/Instance_Insertion
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NeurIPS 2018
Learning to insert an object instance into an image in a semantically coherent manner is a challenging and interesting problem.
GFF: Gated Fully Fusion for Semantic Segmentation
Semantic segmentation generates comprehensive understanding of scenes through densely predicting the category for each pixel.
SlimYOLOv3: Narrower, Faster and Better for Real-Time UAV Applications
Drones or general Unmanned Aerial Vehicles (UAVs), endowed with computer vision function by on-board cameras and embedded systems, have become popular in a wide range of applications.
Dynamic Multi-Scale Filters for Semantic Segmentation
PaddlePaddle/PaddleSeg
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ICCV 2019
DMNet is composed of multiple Dynamic Convolutional Modules (DCMs) arranged in parallel, each of which exploits context-aware filters to estimate semantic representation for a specific scale.
Strip Pooling: Rethinking Spatial Pooling for Scene Parsing
Andrew-Qibin/SPNet
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CVPR 2020
Spatial pooling has been proven highly effective in capturing long-range contextual information for pixel-wise prediction tasks, such as scene parsing.
CascadePSP: Toward Class-Agnostic and Very High-Resolution Segmentation via Global and Local Refinement
hkchengrex/CascadePSP
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CVPR 2020
In this paper, we propose a novel approach to address the high-resolution segmentation problem without using any high-resolution training data.
Malleable 2.5D Convolution: Learning Receptive Fields along the Depth-axis for RGB-D Scene Parsing
charlesCXK/RGBD_Semantic_Segmentation_PyTorch
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ECCV 2020
In this paper, we propose a novel operator called malleable 2. 5D convolution to learn the receptive field along the depth-axis.
Minimal Solvers for Single-View Lens-Distorted Camera Auto-Calibration
This paper proposes minimal solvers that use combinations of imaged translational symmetries and parallel scene lines to jointly estimate lens undistortion with either affine rectification or focal length and absolute orientation.
Kimera: from SLAM to Spatial Perception with 3D Dynamic Scene Graphs
MIT-SPARK/Kimera
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This mental model captures geometric and semantic aspects of the scene, describes the environment at multiple levels of abstractions (e. g., objects, rooms, buildings), includes static and dynamic entities and their relations (e. g., a person is in a room at a given time).
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.
