SOTAVerified

Scene Parsing

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

Papers

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Showing 1–50 of 199 papers

TitleStatusHype
OneFormer: One Transformer to Rule Universal Image SegmentationCode3
Generalized Robot 3D Vision-Language Model with Fast Rendering and Pre-Training Vision-Language AlignmentCode3
Kimera: from SLAM to Spatial Perception with 3D Dynamic Scene GraphsCode2
OCNet: Object Context Network for Scene ParsingCode2
Robust Shape Fitting for 3D Scene AbstractionCode2
Multi-Grained Contrast for Data-Efficient Unsupervised Representation LearningCode1
Mesh Convolution with Continuous Filters for 3D Surface ParsingCode1
A Dense Material Segmentation Dataset for Indoor and Outdoor Scene ParsingCode1
Traffic Scene Parsing through the TSP6K DatasetCode1
Sketching Image Gist: Human-Mimetic Hierarchical Scene Graph GenerationCode1
Plane Geometry Diagram ParsingCode1
CascadePSP: Toward Class-Agnostic and Very High-Resolution Segmentation via Global and Local RefinementCode1
Uni-3D: A Universal Model for Panoptic 3D Scene ReconstructionCode1
BORM: Bayesian Object Relation Model for Indoor Scene RecognitionCode1
Pyramidal Convolution: Rethinking Convolutional Neural Networks for Visual RecognitionCode1
Resource Efficient Mountainous Skyline Extraction using Shallow LearningCode1
Evidential fully convolutional network for semantic segmentationCode1
Pyramid Scene Parsing NetworkCode1
RT-K-Net: Revisiting K-Net for Real-Time Panoptic SegmentationCode1
Semantic Flow for Fast and Accurate Scene ParsingCode1
GFF: Gated Fully Fusion for Semantic SegmentationCode1
TO-Scene: A Large-scale Dataset for Understanding 3D Tabletop ScenesCode1
Minimal Solvers for Single-View Lens-Distorted Camera Auto-CalibrationCode1
Inter-GPS: Interpretable Geometry Problem Solving with Formal Language and Symbolic ReasoningCode1
AttaNet: Attention-Augmented Network for Fast and Accurate Scene ParsingCode1
Malleable 2.5D Convolution: Learning Receptive Fields along the Depth-axis for RGB-D Scene ParsingCode1
DPF: Learning Dense Prediction Fields with Weak SupervisionCode1
Global Aggregation then Local Distribution for Scene ParsingCode1
Part-aware Panoptic SegmentationCode1
Panoptic SegmentationCode1
VIBUS: Data-efficient 3D Scene Parsing with VIewpoint Bottleneck and Uncertainty-Spectrum ModelingCode1
Context-Aware Synthesis and Placement of Object InstancesCode1
3D-to-2D Distillation for Indoor Scene ParsingCode1
Edge-aware Guidance Fusion Network for RGB Thermal Scene ParsingCode1
EPSNet: Efficient Panoptic Segmentation Network with Cross-layer Attention FusionCode1
Pointly-supervised 3D Scene Parsing with Viewpoint BottleneckCode1
EGFNet: Edge-Aware Guidance Fusion Network for RGB–Thermal Urban Scene ParsingCode1
Editable Free-viewpoint Video Using a Layered Neural RepresentationCode1
Strip Pooling: Rethinking Spatial Pooling for Scene ParsingCode1
Fast and Accurate Scene Parsing via Bi-direction Alignment NetworksCode1
Deep Dual-resolution Networks for Real-time and Accurate Semantic Segmentation of Road ScenesCode1
Boosting Night-time Scene Parsing with Learnable FrequencyCode1
Complete 3D Scene Parsing from an RGBD ImageCode0
PIG: Prompt Images Guidance for Night-Time Scene ParsingCode0
LF Tracy: A Unified Single-Pipeline Approach for Salient Object Detection in Light Field CamerasCode0
Holistic 3D Scene Parsing and Reconstruction from a Single RGB ImageCode0
HAPNet: Toward Superior RGB-Thermal Scene Parsing via Hybrid, Asymmetric, and Progressive Heterogeneous Feature FusionCode0
Improving Panoptic Segmentation for Nighttime or Low-Illumination Urban Driving ScenesCode0
Unlocking the Full Potential of Small Data with Diverse SupervisionCode0
DeLS-3D: Deep Localization and Segmentation with a 3D Semantic MapCode0
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All datasetsPGDP5KCityscapes test

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1PGDPNetTotal Accuracy84.7—Unverified
2Inter-GPSTotal Accuracy27.3—Unverified
#ModelMetricClaimedVerifiedStatus
1VCD No CoarsemIoU82.3—Unverified