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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1076–1100 of 1723 papers

TitleStatusHype
P4Contrast: Contrastive Learning with Pairs of Point-Pixel Pairs for RGB-D Scene Understanding—0
PAD-Net: Multi-Tasks Guided Prediction-and-Distillation Network for Simultaneous Depth Estimation and Scene Parsing—0
PADriver: Towards Personalized Autonomous Driving—0
PAg-NeRF: Towards fast and efficient end-to-end panoptic 3D representations for agricultural robotics—0
PanoContext-Former: Panoramic Total Scene Understanding with a Transformer—0
PanoGS: Gaussian-based Panoptic Segmentation for 3D Open Vocabulary Scene Understanding—0
PanoMixSwap Panorama Mixing via Structural Swapping for Indoor Scene Understanding—0
CoT-Drive: Efficient Motion Forecasting for Autonomous Driving with LLMs and Chain-of-Thought Prompting—0
Learning Segmented 3D Gaussians via Efficient Feature Unprojection for Zero-shot Neural Scene Segmentation—0
CoPa-SG: Dense Scene Graphs with Parametric and Proto-Relations—0
Convolutional Patch Networks with Spatial Prior for Road Detection and Urban Scene Understanding—0
Contextual Self-paced Learning for Weakly Supervised Spatio-Temporal Video Grounding—0
Panoptic Out-of-Distribution Segmentation—0
Panoptic Perception: A Novel Task and Fine-grained Dataset for Universal Remote Sensing Image Interpretation—0
PanopticRecon: Leverage Open-vocabulary Instance Segmentation for Zero-shot Panoptic Reconstruction—0
Context-Dependent Diffusion Network for Visual Relationship Detection—0
Panoptic Segmentation Meets Remote Sensing—0
PanopticSplatting: End-to-End Panoptic Gaussian Splatting—0
Context-Aware Human Behavior Prediction Using Multimodal Large Language Models: Challenges and Insights—0
Wireless Sensing With Deep Spectrogram Network and Primitive Based Autoregressive Hybrid Channel Model—0
Content-Aware Preserving Image Generation—0
Configurable 3D Scene Synthesis and 2D Image Rendering with Per-Pixel Ground Truth using Stochastic Grammars—0
Real-time Approximate Bayesian Computation for Scene Understanding—0
PAPooling: Graph-based Position Adaptive Aggregation of Local Geometry in Point Clouds—0
VideoGameBunny: Towards vision assistants for video games—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