Learning Representations and Generative Models for 3D Point Clouds
Authors: Panos Achlioptas, Olga Diamanti, Ioannis Mitliagkas, Leonidas Guibas
ICML 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In Section 5, we perform comprehensive experiments evaluating all of our models both quantitatively and qualitatively. |
| Researcher Affiliation | Academia | 1Department of Computer Science, Stanford University, USA 2MILA, Department of Computer Science and Operations Research, University of Montr eal, Canada. |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Last, the code for all our models is publicly available1. 1http://github.com/optas/latent_3d_points |
| Open Datasets | Yes | In all experiments in the main paper, we use shapes from the Shape Net repository (Chang et al., 2015), that are axis aligned and centered into the unit sphere. |
| Dataset Splits | Yes | Unless otherwise stated, we train models with point clouds from a single object class and work with train/validation/test sets of an 85%-5%-10% split. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU/CPU models, processor types, or cloud computing resources used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details, such as library names with version numbers, needed to replicate the experiment. |
| Experiment Setup | Yes | The input to our AE network is a point cloud with 2048 points (2048 3 matrix). The encoder architecture follows the design principle of (Qi et al., 2016a): 1-D convolutional layers with kernel size 1 and an increasing number of features; this approach encodes every point independently. In our implementation we use 5 1-D convolutional layers, each followed by a Re LU (Nair & Hinton, 2010) and a batch-normalization layer (Ioffe & Szegedy, 2015). Our decoder transforms the latent vector using 3 fully connected layers, the first two having Re LUs, to produce a 2048 3 output. In the remainder of the paper, unless otherwise stated, we use an AE with a 128-dimensional bottleneck layer. The architecture of the discriminator is identical to the AE (modulo the filter-sizes and number of neurons), without any batch-norm and with leaky Re LUs (Maas et al., 2013) instead or Re LUs. The generator takes as input a Gaussian noise vector and maps it to a 2048 3 output via 5 FC-Re LU layers. |