Instace-Based AMN Classification for Improved Object Recognition in 2D and 3D Laser Range Data

By Rudolph Triebel, Richard Schmidt, Óscar Martínez Mozos, Wolfram Burgard. 2007

67 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Bo Li
2. Dieter Fox
3. Liefeng Bo
4. Xiaofeng Ren
5. Sergey Levine
6. Jitendra Malik
7. Yoshua Bengio
8. Trevor Darrell

Citations in the corpus

• 3D fully convolutional network for vehicle detection in point cloud (2016, background): “Candidates can be proposed by delicate segmentation algorithms [2], [5], [11], [14], [21], [22], [28], [29], [31], sliding window [30], random sampling [13], or the recently popular Region Proposal Network (RPN) [26].”
• Vehicle Detection from 3D Lidar Using Fully Convolutional Network (2016, background): “More delicate segmentation can be obtained by forming graphs on the point cloud [32, 14, 21, 29, 30].”
• NON-ASSOCIATIVE MARKOV NETWORKS FOR 3D POINT CLOUD CLASSIFICATION (2010, unknown): “In (Triebel et al., 2007) the algorithm is revisited.”
• Unsupervised feature learning for 3D scene labeling (2014, background): “There is a large body of literature on 3D scene labeling in indoor environments ([1], [2], [3], [4], [5]).”
• Learning semantic place labels from occupancy grids using CNNs (2016, background): “Additionally, it has been shown that associative Markov networks (AMNs) can be useful in improving classification results in the context of map-annotation [12], [11].”
• Towards 3D object recognition via classification of arbitrary object tracks (2011, background): “First, multi-class object recognition in static scenes continues to be an active area of research, e.g. in [2], [3], [4], [5], [6].”
• Contextual classification with functional Max-Margin Markov Networks (2009, methodology): “AMNs have been demonstrated to work well on the task of 3-D point cloud classification [1, 28 , 16].”
• Detection-based object labeling in 3D scenes (2012, background): “[29] built on associative Markov networks introduced in [2] to detect large objects such as chairs, tables, and fans in 3D laser scans.”