Incremental Scene Synthesis
Authors: Benjamin Planche, Xuejian Rong, Ziyan Wu, Srikrishna Karanam, Harald Kosch, YingLi Tian, Jan Ernst, ANDREAS HUTTER
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate efficacy on various 2D as well as 3D data. We demonstrate our solution on various synthetic and real 2D and 3D environments. Table 1: Quantitative comparison on 2D and 3D scenes... Table 2: Ablation study on Celeb A... |
| Researcher Affiliation | Collaboration | 1Siemens Corporate Technology, Munich, Germany 2University of Passau, Passau, Germany 3The City College, City University of New York, New York NY 4Siemens Corporate Technology, Princeton NJ |
| Pseudocode | No | No pseudocode or algorithm block is present. |
| Open Source Code | No | No statement is made about the availability of source code, nor is a link provided. |
| Open Datasets | Yes | We use a synthetic dataset of indoor 83 83 floor plans rendered using the Ho ME platform [2] and SUNCG data [20] (8,640 training + 2,240 test images from random rooms office', living', and bedroom'). Similar to Fraccaro et al. [8], we also consider an agent exploring real pictures from the Celeb A dataset [13]... As a first 3D experiment, we recorded, with the Vizdoom platform [27]... We then consider the Active Vision Dataset (AVD) [1]... |
| Dataset Splits | Yes | 8,640 training + 2,240 test images from random rooms... 34 training and 6 testing episodes... We selected 15 [scenes] for training and 4 for testing as suggested by the dataset authors... |
| Hardware Specification | Yes | Note that on a Nvidia Titan X, the whole process (registering 5 views, localizing the agent, recalling the 5 images, and generating 5 new ones) takes less than 1s. |
| Software Dependencies | No | The paper mentions platforms like 'Vizdoom' but does not specify version numbers for any software dependencies like programming languages, libraries, or frameworks. |
| Experiment Setup | No | The paper describes experimental datasets and agent characteristics but does not provide specific hyperparameter values (e.g., learning rate, batch size, number of epochs) or other detailed training configurations. |