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 1001–1010 of 1723 papers

TitleStatusHype
PAg-NeRF: Towards fast and efficient end-to-end panoptic 3D representations for agricultural robotics—0
Can you text what is happening? Integrating pre-trained language encoders into trajectory prediction models for autonomous driving—0
Weakly Supervised Point Clouds Transformer for 3D Object Detection—0
Structural Concept Learning via Graph Attention for Multi-Level Rearrangement Planning—0
Expanding Frozen Vision-Language Models without Retraining: Towards Improved Robot Perception—0
Semi-Supervised Semantic Depth Estimation using Symbiotic Transformer and NearFarMix Augmentation—0
Synergizing Contrastive Learning and Optimal Transport for 3D Point Cloud Domain Adaptation—0
End-to-end Autonomous Driving using Deep Learning: A Systematic Review—0
SurGNN: Explainable visual scene understanding and assessment of surgical skill using graph neural networks—0
Novel-view Synthesis and Pose Estimation for Hand-Object Interaction from Sparse Views—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