Scale-hierarchical 3D object recognition in cluttered scenes

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How researchers see this paper

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

VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection (2017, methodology): “Some of the earlier methods for feature representation are [41, 8, 7, 19, 42, 35, 6, 27, 1, 36, 2, 25, 26].”
MVX-Net: Multimodal VoxelNet for 3D Object Detection (2019, background): “Earlier approaches ([6], [36], [1], [8], [37]) employ hand-crafted representations and achieve satisfactory results in the presence of rich and detailed 3D information.”
A Global Hypotheses Verification Method for 3D Object Recognition (2012, methodology): “This cue was exploited by thresholding scene points based on a fixed distance value in the HV stage proposed in [1, 3, 6, 5].”
Rotational Projection Statistics for 3D Local Surface Description and Object Recognition (2013, background): “Descriptiveness and robustness of a feature descriptor are crucial for accurate feature matching (Bariya and Nishino, 2010).”
Learning to Rank 3D Features (2014, background): “Many of them are based on selecting salient points in the 3D point cloud and using feature representations that can invariantly describe regions around the points [29,15,27,8,2,1,26].”
3D Object Recognition in Cluttered Scenes with Local Surface Features: A Survey (2014, methodology): “Examples of the methods based on this technique include [34] and [48].”