Contextually guided semantic labeling and search for three-dimensional point clouds
By Abhishek Anand, Hema Swetha Koppula, Thorsten Joachims, Ashutosh Saxena. 2013
200 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
- Marc Pollefeys
- Vladlen Koltun
- Shuran Song
- Torsten Sattler
- Jianxiong Xiao
- Dieter Fox
- Jitendra Malik
- Josef Sivic
In Computer Vision and Pattern Recognition
- Luc Van Gool
- Jitendra Malik
- Ming-Hsuan Yang
- Trevor Darrell
- Dacheng Tao
- Andrew Zisserman
- Xiaogang Wang
- Andrea Vedaldi
Citations in the corpus
- Tangent Convolutions for Dense Prediction in 3D (2018, background): “A popular line of work exploits graphical models, including conditional random fields [38, 11, 2, 58, 19, 27, 56].”
- SUN RGB-D: A RGB-D scene understanding benchmark suite (2015, background): “The Cornell RGBD dataset [2] [34] contains 52 indoors scenes with per-point annotations on the stitched point clouds.”
- The role of RGB-D benchmark datasets: an overview (2013, background): “They provide seven different scenes including RGB, depth and pose data in a txt-file.”
- Semantic mapping for mobile robotics tasks: A survey (2015, background): “Similarly, Anand et al. [97] proposed a semantic labeling and search of 3D point clouds based on RGB-D data.”
- Large-Scale Point Cloud Semantic Segmentation with Superpoint Graphs (2017, methodology): “The solution is then spatially regularized using graphical models [37, 24, 34, 44, 21, 2, 38, 35, 49] or structured optimization [27].”
- Pedestrian detection combining RGB and dense LIDAR data (2014, background): “Kinect provides a dense depth map registered with an RGB image (RGB-D) on which standard detection machinery can be applied and this has stimulated much recent research in object detection [7],[8] articulated pose prediction [9] and scene understanding [10].”
- InLoc: Indoor Visual Localization with Dense Matching and View Synthesis (2019, background): “With the increased availability of laser range scanners and time-of-flight (ToF) sensors, several datasets include depth data besides RGB images [4], [9],”
- Unsupervised feature learning for 3D scene labeling (2014, background): “3D point clouds contain very important shape and spatial information, which allows for models that take advantage of context and spatial relationships between objects [8], [9].”