Spherical Criteria for Fast and Accurate 360° Object Detection
Authors: Pengyu Zhao, Ansheng You, Yuanxing Zhang, Jiaying Liu, Kaigui Bian, Yunhai Tong12959-12966
AAAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | To validate the design of spherical criteria and Reprojection R-CNN, we construct two unbiased synthetic datasets for training and evaluation. Experimental results reveal that compared with the existing criteria, the two-stage detector with spherical criteria achieves the best m AP results under the same inference speed, demonstrating that the spherical criteria can be more suitable for 360 object detection. |
| Researcher Affiliation | Academia | School of EECS, Peking University, Beijing, China {pengyuzhao, youansheng, longo, liujiaying, bkg, yhtong}@pku.edu.cn |
| Pseudocode | No | The paper describes methods textually and with diagrams, but no structured pseudocode or algorithm blocks are provided. |
| Open Source Code | No | The paper does not provide any links to open-source code for the described methodology or state that the code is publicly released. |
| Open Datasets | Yes | VOC360 is a synthetic dataset generated from PASCAL VOC 2007 and 2012 (Everingham et al. 2010)... For the multi-object scenario, we construct COCO-Men dataset, which combines the real-world background 360 images and the segmented images of people cropped from COCO dataset (Lin et al. 2014)... To demonstrate the capability of Rep R-CNN for real scenes, we use SUN360 dataset (Xiao et al. 2012). |
| Dataset Splits | Yes | VOC360 has 15000 training images, 1800 validation images, and 4955 test images. |
| Hardware Specification | No | Both Sph RPN and Rep Net are trained on 4 GPUs for 20 epochs... (No specific GPU model, CPU, or other hardware details are provided). |
| Software Dependencies | No | The paper mentions using VGG-16 as a backbone network and refers to concepts like Faster R-CNN, but it does not specify any software names with version numbers (e.g., PyTorch 1.x, TensorFlow 2.x, Python 3.x). |
| Experiment Setup | Yes | The learning rate is initially set to 0.001 and then decreased by a factor of 10 after training 15 epochs. The batch size is 16 in Sph RPN and 128 in Rep Net. |