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 671–680 of 1723 papers

TitleStatusHype
UniQ: Unified Decoder with Task-specific Queries for Efficient Scene Graph Generation—0
Vision-Language Models for Autonomous Driving: CLIP-Based Dynamic Scene Understanding—0
A Systematic Literature Review on Deep Learning-based Depth Estimation in Computer Vision—0
NextStop: An Improved Tracker For Panoptic LIDAR Segmentation DataCode0
TADFormer : Task-Adaptive Dynamic Transformer for Efficient Multi-Task Learning—0
Advancing the Understanding of Fine-Grained 3D Forest Structures using Digital Cousins and Simulation-to-Reality: Methods and Datasets—0
LargeAD: Large-Scale Cross-Sensor Data Pretraining for Autonomous Driving—0
CL3DOR: Contrastive Learning for 3D Large Multimodal Models via Odds Ratio on High-Resolution Point Clouds—0
IAM: Enhancing RGB-D Instance Segmentation with New BenchmarksCode0
3D-LLaVA: Towards Generalist 3D LMMs with Omni Superpoint Transformer—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