CPMC-3D-O2P: Semantic segmentation of RGB-D images using CPMC and Second Order Pooling

By Dan Banica, Cristian Sminchisescu. 2013

21 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, 3D Vision & Pose

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Citations in the corpus

Object-Proposal Evaluation Protocol is ‘Gameable’ (2015, methodology): “Object proposals are evaluated using this metric in two ways: – plotting Recall-vs-#proposals by ﬁxing t – plotting Recall-vs-t by ﬁxing the #proposals in L .”
Learning Rich Features from RGB-D Images for Object Detection and Segmentation (2014, background): “mentation system we proposed in [18]. This approach obtains state-of-the-art results for that task, as well. 1.1 Related Work Most prior work on RGB-D perception has focussed on semantic segmentation [3,18,24,31,34], i.e. the task of assigning a category label to each pixel. While Learning Rich Feat”
Inferring 3D Object Pose in RGB-D Images (2015, background): “There have been corresponding works for RGB-D images studying the problem of object detection [17, 34, 20, 21, 35, 13, 33, 19, 23, 4], semantic segmentation [3, 12, 18, 24, 31, 13, 32], and more recently instance segmentation [13, 32].”
Monocular 3D Object Detection for Autonomous Driving (2016, background): “In RGB-D, [10] exploited stereo imagery to exhaustively scored 3D bounding boxes using a conditional random field with several depth-informed potentials.”
3D Object Proposals Using Stereo Imagery for Accurate Object Class Detection (2016, background): “bles object detectors to leverage the great power of deep neural networks [1], [2]. Numerous works have been proposed for different modalities, i.e., RGB [7], [8], [10], [11], [13], [20], RGB-D [18], [21], [22], [23], and video [24], [25]. In RGB, one typical paradigm is to generate candidate segmen”
Aligning 3D models to RGB-D images of cluttered scenes (2015, background): “There have been corresponding works for RGB-D images studying the problems of object detection [4, 13, 17, 20, 21, 22, 24, 34, 35, 36], semantic segmentation [3, 12, 13, 19, 25, 32, 33], and more recently instance segmentation [13, 33].”
Second-order constrained parametric proposals and sequential search-based structured prediction for semantic segmentation in RGB-D images (2015, background): “estimation[42], Kinect has also spurred a wave of scene understanding research in robotics[39, 45] and computer vision[44, 39, 18, 34, 2, 3] with datasets[43, 28] recently made available.”
3D Object Proposals for Accurate Object Class Detection (2015, background): “[17, 18] extend CPMC [10] with additional affinities that encourage the proposals to respect occlusion boundaries.”