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 1171–1180 of 1723 papers

TitleStatusHype
Segmentation-guided Domain Adaptation for Efficient Depth Completion—0
Segment-Fusion: Hierarchical Context Fusion for Robust 3D Semantic Segmentation—0
Self-Supervised and Generalizable Tokenization for CLIP-Based 3D Understanding—0
Self-supervised Learning of Occlusion Aware Flow Guided 3D Geometry Perception with Adaptive Cross Weighted Loss from Monocular Videos—0
Self-supervised Learning via Cluster Distance Prediction for Operating Room Context Awareness—0
Self-Supervised Object Detection from Egocentric Videos—0
Self-supervised Pre-training with Masked Shape Prediction for 3D Scene Understanding—0
Self-Supervised Relative Depth Learning for Urban Scene Understanding—0
SELMA: SEmantic Large-scale Multimodal Acquisitions in Variable Weather, Daytime and Viewpoints—0
Semantic Augmented Reality Environment with Material-Aware Physical Interactions—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