SOTAVerified

Scene Understanding

Scene understanding involves interpreting the visual information of a scene, including objects, their spatial relationships, and the overall layout. It goes beyond simple object recognition by considering the context and how objects relate to each other and the environment.

Papers

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Showing 1281–1290 of 1723 papers

TitleStatusHype
ePointDA: An End-to-End Simulation-to-Real Domain Adaptation Framework for LiDAR Point Cloud Segmentation—0
Towards Semantic Segmentation of Urban-Scale 3D Point Clouds: A Dataset, Benchmarks and ChallengesCode1
On the Structures of Representation for the Robustness of Semantic Segmentation to Input CorruptionCode0
Deep Learning Techniques for Geospatial Data Analysis—0
Minimal Adversarial Examples for Deep Learning on 3D Point Clouds—0
TORNADO-Net: mulTiview tOtal vaRiatioN semAntic segmentation with Diamond inceptiOn module—0
m2caiSeg: Semantic Segmentation of Laparoscopic Images using Convolutional Neural NetworksCode0
MLM: A Benchmark Dataset for Multitask Learning with Multiple Languages and ModalitiesCode0
DAWN: Vehicle Detection in Adverse Weather Nature Dataset—0
Factor Graph based 3D Multi-Object Tracking in Point Clouds—0
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All datasetsSemantic Scene Understanding Challenge (passive actuation & ground-truth localisation)ADE20K valSemantic Scene Understanding Challenge (active actuation & ground-truth localisation)

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ACRV BaselineOMQ0.44—Unverified
2Team VGAI (TCS Research)OMQ0.37—Unverified
3Demo_semantic_SLAMOMQ0.11—Unverified
#ModelMetricClaimedVerifiedStatus
1CPN(ResNet-101)Mean IoU46.3—Unverified
#ModelMetricClaimedVerifiedStatus
1ACRV BaselineOMQ0.35—Unverified