Segmentation of unknown objects in indoor environments

By Andreas Richtsfeld, Thomas Morwald, Johann Prankl, Michael Zillich, Markus Vincze. 2012

180 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

1. Dieter Fox
2. Wolfram Burgard
3. Rudolph Triebel
4. Ling Shao
5. Li Liu
6. Arsalan Mousavian
7. Jungong Han
8. Ziyun Cai

In Computer Vision and Pattern Recognition

1. Ling Shao
2. Luc Van Gool
3. Dieter Fox
4. Jitendra Malik
5. Dacheng Tao
6. Alan L. Yuille
7. Xiaogang Wang
8. Trevor Darrell

Citations in the corpus

• RGB-D datasets using microsoft kinect or similar sensors: a survey (2016, background): “3 Object segmentation dataset (OSD) Vision for robotics group in Vienna University of Technology created Object Segmentation dataset in 2012 for the evaluation of segmenting unknown objects from generic scenes [72].”
• InstaBoost: Boosting Instance Segmentation via Probability Map Guided Copy-Pasting (2019, background): “It plays an vital role in computer vision and has many practical applications in autonomous driving [10], robotic manipulation [38], HOI detection [29, 36] [28, 23] for the problem.”
• PR2 looking at things — Ensemble learning for unstructured information processing with Markov logic networks (2014, background): “Approaches that deal with object recognition in more cluttered scenes include [4], [5], [6].”
• Unseen Object Instance Segmentation for Robotic Environments (2020, methodology): “We evaluate quantitatively and qualitatively on two real-world datasets: OCID [79] and OSD [80], which have 2346 images of semi-automatically constructed labels and 111 manually labeled images, respectively.”
• Learning RGB-D Feature Embeddings for Unseen Object Instance Segmentation (2020, methodology): “The network is trained with the Adam optimizer [33] for 16 epochs on the Tabletop Object Dataset with batch size 16 and learning rate 1e-5.”
• Learning of perceptual grouping for object segmentation on RGB-D data (2014, background): “Compared to our previous work in [31], the perceptual grouping of RGB-D data over different abstraction levels is discussed in detail.”
• Unknown Object Segmentation from Stereo Images (2021, methodology): “There exist several datasets of objects on table-top surfaces, and many provide segmentation [36], [60], bounding box [61], [62] or point cloud [63] annotations.”
• SUN RGB-D: A RGB-D scene understanding benchmark suite (2015, unknown): “There are many existing RGB-D datasets available [54] [47] [1] [25] [44] [60] [49] [45] [29] [66] [57] [52] [46] [16] [21] [73] [35] [67] [3] [41] [10] [63] [42] [64] [65] [48] [12] [33] [8] [50] [26] [6].”
• Learning to Singulate Objects using a Push Proposal Network (2017, methodology): “Similar to them, we also create object hypothesis from over-segmenting objects into surface facets but use a different method based on the work of [25].”