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 [1].
PerspectiveNet: 3D Object Detection from a Single RGB Image via Perspective Points
Authors: Siyuan Huang, Yixin Chen, Tao Yuan, Siyuan Qi, Yixin Zhu, Song-Chun Zhu
NeurIPS 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on SUN RGB-D dataset show that the proposed method significantly outperforms existing RGB-based approaches for 3D object detection. |
| Researcher Affiliation | Academia | Siyuan Huang Department of Statistics EMAIL Yixin Chen Department of Statistics EMAIL Tao Yuan Department of Statistics EMAIL Siyuan Qi Department of Computer Science EMAIL Yixin Zhu Department of Statistics EMAIL Song-Chun Zhu Department of Statistics EMAIL |
| Pseudocode | No | The paper describes algorithms and formulations in text and mathematical equations but does not provide any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper states: 'We implement our framework based on the code of Massa and Girshick [83].' This indicates they used an existing framework, but there is no explicit statement that their specific implementation of Perspective Net is open-source or a link to its repository. |
| Open Datasets | Yes | We conduct comprehensive experiments on SUN RGB-D [46] dataset. |
| Dataset Splits | No | The paper mentions 4783 training images and 4220 test images but does not explicitly describe a validation dataset split or its size. |
| Hardware Specification | Yes | We use SGD for optimization with a batch size of 32 on a desktop with 4 Nvidia TITAN RTX cards (8 images each card). |
| Software Dependencies | No | The paper states, 'We implement our framework based on the code of Massa and Girshick [83].' While this implies the use of PyTorch (as indicated by the reference title 'maskrcnn-benchmark...in PyTorch'), it does not specify exact version numbers for PyTorch or any other software dependencies, which is required for reproducibility. |
| Experiment Setup | Yes | We resize the images so that the shorter edges are all 800 pixels. To avoid over-fitting, a data augmentation procedure is performed by randomly flipping the images or randomly shifting the 2D bounding boxes with corresponding labels during the training. We use SGD for optimization with a batch size of 32 [...] The learning rate starts at 0.01 and decays by 0.1 at 30,000 and 35,000 iterations. |