Object classification in RGB-D images using Ensamble of Shape Functions mehtod

Klasifikacija objekata prikazanim na 2D ili 3D slikama postaje sve više i više popularna i tehnilogije koje se razvijaju su efikasnije i jednostavnije, dok kvaliteta i točnost ostaju visoki. Metoda korištena u ovom radu je ESF(Ensemble of shape functions). Ona je jedna od metoda uključena u PCL biblioteku. ESF je funkcija oblika koja je jednostavna, a omogućuje puno načina za korištenje. Jedan od najčešćih načina korištenja je klasificiranje objekata, ali ima i ostalih kao što su primjećivanje, računanje raznoraznih udaljenosti, normala u geometrijskoj okolini. U radu je također opisan program za klasifikaciju objekata na RGB-D slikama primjenom ESF metode, te ispitan na ispitnom podatkovnom skupu 3DNet.Object recognition for 2D or 3D images becomes more and more popular and technologies that are being developed are more efficiend and simpler, while quality and precision remain high. The method used in this project is ESF(Ensemble of shape functions). It is one of many methodes included in the PCL library. ESF is shape function that is simple and has various applications. This method is primarily designed for object recognition, but there are other applications like registration, calculating distances or calculating normals in geometric environment. In this project, a program for object classification using ESF method is described and it is tested on the 3DNet data set

Generative and Discriminative Voxel Modeling with Convolutional Neural Networks

When working with three-dimensional data, choice of representation is key. We explore voxel-based models, and present evidence for the viability of voxellated representations in applications including shape modeling and object classification. Our key contributions are methods for training voxel-based variational autoencoders, a user interface for exploring the latent space learned by the autoencoder, and a deep convolutional neural network architecture for object classification. We address challenges unique to voxel-based representations, and empirically evaluate our models on the ModelNet benchmark, where we demonstrate a 51.5% relative improvement in the state of the art for object classification.Comment: 9 pages, 5 figures, 2 table

V2V-PoseNet: Voxel-to-Voxel Prediction Network for Accurate 3D Hand and Human Pose Estimation from a Single Depth Map

Most of the existing deep learning-based methods for 3D hand and human pose estimation from a single depth map are based on a common framework that takes a 2D depth map and directly regresses the 3D coordinates of keypoints, such as hand or human body joints, via 2D convolutional neural networks (CNNs). The first weakness of this approach is the presence of perspective distortion in the 2D depth map. While the depth map is intrinsically 3D data, many previous methods treat depth maps as 2D images that can distort the shape of the actual object through projection from 3D to 2D space. This compels the network to perform perspective distortion-invariant estimation. The second weakness of the conventional approach is that directly regressing 3D coordinates from a 2D image is a highly non-linear mapping, which causes difficulty in the learning procedure. To overcome these weaknesses, we firstly cast the 3D hand and human pose estimation problem from a single depth map into a voxel-to-voxel prediction that uses a 3D voxelized grid and estimates the per-voxel likelihood for each keypoint. We design our model as a 3D CNN that provides accurate estimates while running in real-time. Our system outperforms previous methods in almost all publicly available 3D hand and human pose estimation datasets and placed first in the HANDS 2017 frame-based 3D hand pose estimation challenge. The code is available in https://github.com/mks0601/V2V-PoseNet_RELEASE.Comment: HANDS 2017 Challenge Frame-based 3D Hand Pose Estimation Winner (ICCV 2017), Published at CVPR 201