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 1451–1475 of 1723 papers

TitleStatusHype
Implicit Background Estimation for Semantic SegmentationCode0
Spatial Sampling Network for Fast Scene Understanding—0
Bridging Stereo Matching and Optical Flow via Spatiotemporal CorrespondenceCode0
Real-time Approximate Bayesian Computation for Scene Understanding—0
Unsupervised Domain Adaptation using Generative Adversarial Networks for Semantic Segmentation of Aerial ImagesCode0
A Joint Convolutional Neural Networks and Context Transfer for Street Scenes Labeling—0
An Information-Theoretic Metric of Transferability for Task Transfer LearningCode0
Reasoning About Physical Interactions with Object-Centric Models—0
Segmenting the FutureCode0
DirectShape: Direct Photometric Alignment of Shape Priors for Visual Vehicle Pose and Shape Estimation—0
Deep Optics for Monocular Depth Estimation and 3D Object Detection—0
DSNet: An Efficient CNN for Road Scene Segmentation—0
Deep Surface Normal Estimation with Hierarchical RGB-D FusionCode0
Structured agents for physical construction—0
Deep Reinforcement Learning on a Budget: 3D Control and Reasoning Without a SupercomputerCode0
SemanticKITTI: A Dataset for Semantic Scene Understanding of LiDAR SequencesCode0
JSIS3D: Joint Semantic-Instance Segmentation of 3D Point Clouds with Multi-Task Pointwise Networks and Multi-Value Conditional Random FieldsCode0
ResUNet-a: a deep learning framework for semantic segmentation of remotely sensed dataCode0
Road Scene Understanding by Occupancy Grid Learning from Sparse Radar Clusters using Semantic SegmentationCode0
Do Deep Neural Networks Model Nonlinear Compositionality in the Neural Representation of Human-Object Interactions?—0
Auto-Embedding Generative Adversarial Networks for High Resolution Image SynthesisCode0
Veritatem Dies Aperit- Temporally Consistent Depth Prediction Enabled by a Multi-Task Geometric and Semantic Scene Understanding ApproachCode0
Scene Understanding for Autonomous Manipulation with Deep Learning—0
Monocular 3D Object Detection with Pseudo-LiDAR Point CloudCode0
Quantitative Depth Quality Assessment of RGBD Cameras At Close Range Using 3D Printed FixturesCode0
Show:102550
← PrevPage 59 of 69Next →
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