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 1551–1575 of 1723 papers

TitleStatusHype
A Minimalist Approach to Type-Agnostic Detection of Quadrics in Point Clouds—0
Trajectory-based Scene Understanding using Dirichlet Process Mixture Model—0
General-Purpose Deep Point Cloud Feature ExtractorCode0
Indoor Scene Understanding in 2.5/3D for Autonomous Agents: A Survey—0
Learning Scene Gist with Convolutional Neural Networks to Improve Object Recognition—0
Structured Label Inference for Visual UnderstandingCode0
Scenarios: A New Representation for Complex Scene Understanding—0
Tensor Comprehensions: Framework-Agnostic High-Performance Machine Learning AbstractionsCode0
Recognizing Material Properties from Images—0
Depth Not Needed - An Evaluation of RGB-D Feature Encodings for Off-Road Scene Understanding by Convolutional Neural Network—0
Object segmentation in depth maps with one user click and a synthetically trained fully convolutional network—0
Boundary Seeking GANs—0
Spatial As Deep: Spatial CNN for Traffic Scene UnderstandingCode0
Self-Supervised Relative Depth Learning for Urban Scene Understanding—0
Why my photos look sideways or upside down? Detecting Canonical Orientation of Images using Convolutional Neural Networks—0
Feature discovery and visualization of robot mission data using convolutional autoencoders and Bayesian nonparametric topic models—0
Small Drone Field Experiment: Data Collection & Processing—0
Grounded Objects and Interactions for Video Captioning—0
Natural Language Guided Visual Relationship Detection—0
HyKo: A Spectral Dataset for Scene Understanding—0
ERFNet: Efficient Residual Factorized ConvNet for Real-time Semantic SegmentationCode0
SceneNet RGB-D: Can 5M Synthetic Images Beat Generic ImageNet Pre-Training on Indoor Segmentation?—0
Semantic Line Detection and Its ApplicationsCode1
The Mapillary Vistas Dataset for Semantic Understanding of Street Scenes—0
Dense RGB-D semantic mapping with Pixel-Voxel neural network—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