@ARTICLE{ iet:/content/journals/10.1049/iet-cvi.2016.0502, author = {Giampaolo Pagnutti}, author = {Ludovico Minto}, author = {Pietro Zanuttigh}, keywords = {surface fitting;segmentation output;nonuniform rational B-spline surfaces;input colour;surface fitting parameters;geometry data;surface fitting accuracy;RGBD data;spectral clustering;CNN;initial over-segmentation;over-segmentation;convolutional neural network;convolutional neural networks;labelled segments;softmax classifier;iterative merging algorithm;CNN features;deep learning technique;geometrical cues;semantic labelling;}, ISSN = {1751-9632}, language = {English}, abstract = {We present an approach for segmentation and semantic labelling of RGBD data exploiting together geometrical cues and deep learning techniques. An initial over-segmentation is performed using spectral clustering and a set of non-uniform rational B-spline surfaces is fitted on the extracted segments. Then a convolutional neural network (CNN) receives in input colour and geometry data together with surface fitting parameters. The network is made of nine convolutional stages followed by a softmax classifier and produces a vector of descriptors for each sample. In the next step, an iterative merging algorithm recombines the output of the over-segmentation into larger regions matching the various elements of the scene. The couples of adjacent segments with higher similarity according to the CNN features are candidate to be merged and the surface fitting accuracy is used to detect which couples of segments belong to the same surface. Finally, a set of labelled segments is obtained by combining the segmentation output with the descriptors from the CNN. Experimental results show how the proposed approach outperforms state-of-the-art methods and provides an accurate segmentation and labelling.}, title = {Segmentation and semantic labelling of RGBD data with convolutional neural networks and surface fitting}, journal = {IET Computer Vision}, issue = {8}, volume = {11}, year = {2017}, month = {December}, pages = {633-642(9)}, publisher ={Institution of Engineering and Technology}, copyright = {© The Institution of Engineering and Technology}, url = {https://digital-library.theiet.org/;jsessionid=nuduym9p23i5.x-iet-live-01content/journals/10.1049/iet-cvi.2016.0502} }