Recovering free space of indoor scenes from a single image

By Varsha Hedau, Derek Hoiem, D.A. Forsyth. 2012

122 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition

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In Aerospace Engineering

In Computer Vision and Pattern Recognition

Citations in the corpus

Monocular Object Instance Segmentation and Depth Ordering with CNNs (2015, background): “This yields instance segmentation as well as depth ordering, however, segmentation is typically limited to a simple cuboid-like object shape.”
Don't just listen, use your imagination: Leveraging visual common sense for non-visual tasks (2015, background): “One instantiation of visual common sense that has been leveraged in the vision community in the past is the use of contextual reasoning for improved recognition [22, 12, 21, 17, 25, 50].”
3D Scene Understanding by Voxel-CRF (2013, methodology): “Experimental results show that our method achieves competitive accuracy in inferring scene semantics and visually appealing results in improving the quality of the 3D reconstruction.”
Estimating the 3D Layout of Indoor Scenes and Its Clutter from Depth Sensors (2013, methodology): “Among other applications, room layouts have been used in [7] to predict affordances and in [12, 20] to estimate the free space.”
Efficient Exact Inference for 3D Indoor Scene Understanding (2012, background): “Indoor scene understanding approaches reason about the layout of rooms in the form of 3D cuboids [6–8, 13, 22].”
Localizing 3D cuboids in single-view images (2012, methodology): “Compared with recent approaches that detect cuboids by reasoning about the shape of the entire scene [9, 11, 12, 17, 19, 29], one of the key differences is that we detect cuboids directly without consideration of the global scene geometry.”
Second-order constrained parametric proposals and sequential search-based structured prediction for semantic segmentation in RGB-D images (2015, background): “In this setup [29, 20] analyze the geometry of the rooms including surfaces and objects, whereas [17] reason about object functionality from the standpoint of a human user of the environment.”
Learning Common Sense through Visual Abstraction (2015, background): “A popular use of common-sense knowledge in vision has been for modeling context for improved recognition [10, 14, 17, 18, 20].”
Robust real-time visual odometry for dense RGB-D mapping (2013, background): “In [10], a robot pushes, pokes, pulls and grasps objects with its end-effector, thereby learning about rolling, sliding, etc.”
Cross-View Semantic Segmentation for Sensing Surroundings (2019, background): “Estimating lay-out has been an active topic of research (i.e. room layout estimation [50, 22, 43, 19, 16], free space estimation [17]).”
Holistic Scene Understanding for 3D Object Detection with RGBD Cameras (2013, background): “[11] Varsha Hedau, Derek Hoiem, and David Forsyth.”
Dark, Beyond Deep: A Paradigm Shift to Cognitive AI with Humanlike Common Sense (2020, methodology): “where an algorithm infers the volumetric shapes of objects and relationships (such as occlusion and support) in describing 3D structure and mechanical conﬁgurations. In the next few years, other work [222,223,224,225,226,109,32,227,228,34] also integrated the inference of physical relationships for ”
Structured Prediction of Unobserved Voxels from a Single Depth Image (2016, unknown): “Other work has also made use of trained detectors [25, 55] and semantics [39] to help propose bounding box locations.”
Support Surface Prediction in Indoor Scenes (2013, background): “One line of work is to predict boxy layouts from RGB images [6, 7, 10, 8, 5, 12, 11], in which occlusion and lack of 3D sensors is combatted with simple scene models and strong priors.”
Ieee Transactions on Pattern Analysis and Machine Intelligence 1 3d Reasoning from Blocks to Stability (background): “In addition, researchers have studied indoor environment reasoning on color images, where the 3D geometric inference can be approximated as a Manhattan World [16] [17] [18] [19].”
Indoor Scene Understanding with RGB-D Images: Bottom-up Segmentation, Object Detection and Semantic Segmentation (2015, background): “Thus, [21] recovers walk-able surfaces by reasoning on the location and shape of furniture, [34, 35] reason about the 3D geometry of the room and objects, while [19] focuses on interpreting the scene in a human-centric perspective.”
Perceptual Organization and Recognition of Indoor Scenes from RGB-D Images (2013, background): “The problem of three dimensional scene understanding from monocular images has received considerable attention in recent years [11, 8, 10, 20, 19, 25], and different aspects have been explored.”