Hierarchical Instance Feature Alignment for 2D Image-Based 3D Shape Retrieval
Authors: Heyu Zhou, Weizhi Nie, Wenhui Li, Dan Song, An-An Liu
IJCAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on two popular and novel datasets, MI3DOR and MI3DOR2, validate the superiority of HIFA for 2D imagebased 3D shape retrieval task. |
| Researcher Affiliation | Academia | School of Electrical and Information Engineering, Tianjin University, China zhy_std@163.com, {weizhinie, liwenhui, dan.song}@tju.edu.cn, anan0422@gmail.com |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | Yes | MI3DOR. The MI3DOR dataset [Zhou et al., 2019a] contains 21 categories with 21,000 images and 7,690 3D shapes. |
| Dataset Splits | Yes | There are 10,500 images and 3,842 3D shapes for training, while 10,500 images and 3,848 3D shapes for testing. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper mentions using a 'shared 2D CNN' with 'Alex Net' as the backbone and 'Image Net' for pre-training, but does not provide specific software dependencies with version numbers. |
| Experiment Setup | Yes | We set the view number as 12 and the camera array setting follows [Su et al., 2015]. We empirically set R as 25 by following [Xu et al., 2019] since the lower value is prone to achieve lower accuracy on target domain while a sufficiently large R may lead to the gradient explosion. We set λ, β as 0.05, 1 and γ = 2 / (1+exp(-10p)) - 1, respectively, where p denotes the training progress varying from 0 to 1. |