Active Object Reconstruction Using a Guided View Planner
Authors: Xin Yang, Yuanbo Wang, Yaru Wang, Baocai Yin, Qiang Zhang, Xiaopeng Wei, Hongbo Fu
IJCAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments show that our model (1) increases our reconstruction accuracy with an increasing number of views (2) and generally predicts a more informative sequence of views for object reconstruction compared to other alternative methods. |
| Researcher Affiliation | Academia | 1 Dalian University of Technology 2 City University of Hong Kong xinyang@dlut.edu.cn, yuanbodlut@gmail.com, wangyaru@mail.dlut.edu.cn {ybc, zhangq, xpwei}@dlut.edu.cn, hongbofu@cityu.edu.hk |
| Pseudocode | No | The paper describes the network architecture and methodology but does not include any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide a specific link or explicit statement about the release of their own source code. |
| Open Datasets | Yes | We used the dataset from [Yan et al., 2016], which is based on the Shape Net Core [Wu et al., 2015]. |
| Dataset Splits | No | The paper mentions 'train/test data split' but does not explicitly provide details about a validation set or its split. |
| Hardware Specification | Yes | Our model was trained and tested under the Pytorch framework, accelerated by a GPU (NVIDIA GTX 1080Ti). |
| Software Dependencies | No | The paper mentions using 'Pytorch framework' and 'ADAM solver' but does not specify version numbers for these software dependencies. |
| Experiment Setup | Yes | We updated the weights by using ADAM solver with batchsize 16, epoch 200, λvox = λproj = 0.5. We set λv = 10, λp = 10, λm = 0.04. |