Single-Image 3D Scene Parsing Using Geometric Commonsense
Authors: Chengcheng Yu, Xiaobai Liu, Song-Chun Zhu
IJCAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Results with comparisons on public datasets showed that our method clearly outperforms the alternative methods. |
| Researcher Affiliation | Academia | 1 Department of Statistics, University of California, Los Angeles (UCLA), CA 2 Department of Computer Science, San Diego State Univesity (SDSU), CA chengchengyu@ucla.edu, xiaobai.liu@mail.sdsu.edu, sczhu@stat.ucla.edu |
| Pseudocode | No | The paper describes the inference process but does not include structured pseudocode or an algorithm block. |
| Open Source Code | No | The paper does not provide any concrete statement or link regarding the availability of its source code. |
| Open Datasets | Yes | To evaluate the proposed method, we collect an image dataset that covers five different categories: 1) country; 2) suburban; 3) road; 4) campus; and 5) urban, resulting in a collection of 2,500 images. The urban images mostly follow the Manhattan assumption [Liu et al., 2014] while the country images do not. These images are selected from existing datasets [Hoiem et al., 2008; Liu et al., 2014; Everingham et al., 2015]. |
| Dataset Splits | No | The paper mentions 'For every category, we use 100 images for training and use the rest for testing.', but it does not specify a separate validation split or explicit cross-validation. |
| Hardware Specification | Yes | It costs about 1-2 minutes to converge on a Dell Workstation (i7-4790 CPU@3.6GHZ with 16GB memory). |
| Software Dependencies | No | The paper mentions various methods used (e.g., 'method by Ren et al.', 'method by Li et al.'), but it does not specify software names with version numbers. |
| Experiment Setup | Yes | We set the maximal iteration of DDMCMC to be 2000. |