Estimating the 3D Layout of Indoor Scenes and Its Clutter from Depth Sensors
By Jian Zhang, Chen Kan, Alexander Gerhard Schwing, Raquel Urtasun. 2013
78 citations recorded by the source corpus.
Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Aerospace Engineering
- Matthias Nießner
- Angela Dai
- Thomas A. Funkhouser
- Abhinav Kumar Gupta
- Shuran Song
- Angel X. Chang
- Leonidas J. Guibas
- Charles Ruizhongtai Qi
In Computer Vision and Pattern Recognition
- Luc Van Gool
- Jitendra Malik
- Abhinav Kumar Gupta
- Dacheng Tao
- Trevor Darrell
- Xiaogang Wang
- Ming-Hsuan Yang
- Andrew Zisserman
Citations in the corpus
- Matterport3D: Learning from RGB-D Data in Indoor Environments (2017, methodology): “These previous RGB-D datasets have been used to train models for several standard scene understanding tasks, including semantic segmentation [27, 33, 13, 40, 7], 3D object detection [31, 24, 14, 36, 37], normal estimation [41, 23, 9, 3], camera relocalization [30, 39], and others [47, 11, 12].”
- SUN RGB-D: A RGB-D scene understanding benchmark suite (2015, background): “With the extra depth information in the RGB-D image, this task is considered to be much more feasible [74].”
- Superpixels: An evaluation of the state-of-the-art (2016, methodology): “We suspect that regarding the used metrics, the number of superpixels (K = 200) and the used superpixel algorithm, the effect of depth information might be more pronounced in the experiments presented in [92] compared to ours.”
- Sliding Shapes for 3D Object Detection in Depth Images (2014, background): “RGB-D Scene Understanding: Besides the RGB-D segmentation and detection works mentioned above, [83, 19, 84] proposed to estimate the room layout, support surfaces, and scene understanding for the whole room including objects.”
- Three-Dimensional Object Detection and Layout Prediction Using Clouds of Oriented Gradients (2016, methodology): “Given an RGB-D image, scene parsing requires not only object detection, but also room layout (floor, ceiling, wall) prediction [12, 22, 41, 32].”
- Unfolding an Indoor Origami World (2014, background): “Indeed, on these scenes, it turns out to be difficult even with depth to find a variety of simple primitives such as cuboids [16,17,39], support surfaces [7], or segmentations [9].”