Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
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 | Venue PDF | 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 EMAIL, EMAIL, EMAIL EMAIL, EMAIL |
| 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. |