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 21–30 of 199 papers

TitleStatusHype
A Review and A Robust Framework of Data-Efficient 3D Scene Parsing with Traditional/Learned 3D Descriptors—0
A Data-efficient Framework for Robotics Large-scale LiDAR Scene ParsingCode0
Generalized Robot 3D Vision-Language Model with Fast Rendering and Pre-Training Vision-Language AlignmentCode3
CaveSeg: Deep Semantic Segmentation and Scene Parsing for Autonomous Underwater Cave Exploration—0
RoadFormer: Duplex Transformer for RGB-Normal Semantic Road Scene Parsing—0
CACFNet: Cross-Modal Attention Cascaded Fusion Network for RGB-T Urban Scene Parsing—0
EGFNet: Edge-Aware Guidance Fusion Network for RGB–Thermal Urban Scene ParsingCode1
Improving Panoptic Segmentation for Nighttime or Low-Illumination Urban Driving ScenesCode0
Semantic Segmentation on VSPW Dataset through Contrastive Loss and Multi-dataset Training Approach—0
Recyclable Semi-supervised Method Based on Multi-model Ensemble for Video Scene Parsing—0
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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