Learning Spherical Convolution for Fast Features from 360° Imagery
Authors: Yu-Chuan Su, Kristen Grauman
NeurIPS 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We validate our approach compared to several alternative methods in terms of both raw CNN output accuracy as well as applying a state-of-the-art flat" object detector to 360 data. We evaluate SPHCONV on the Pano2Vid [35] and PASCAL VOC [9] datasets, both for raw convolution accuracy as well as impact on an object detection task. |
| Researcher Affiliation | Academia | Yu-Chuan Su Kristen Grauman The University of Texas at Austin |
| Pseudocode | No | The paper does not include any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper references third-party open-source projects like py-faster-rcnn used in their work, but it does not provide an explicit statement or link for the code implementation of their own proposed methodology. |
| Open Datasets | Yes | We use two datasets: Pano2Vid for training, and Pano2Vid and PASCAL for testing. Pano2Vid: We sample frames from the 360 videos in the Pano2Vid dataset [35] for both training and testing. PASCAL VOC: Because the target model was originally trained and evaluated on PASCAL VOC 2007, we 360-ify it to evaluate the object detector application. |
| Dataset Splits | No | The paper states the number of frames for training (1,056) and testing (168) from the Pano2Vid dataset, but it does not specify a separate validation dataset split. |
| Hardware Specification | No | The paper does not provide specific details regarding the hardware used for running the experiments, such as GPU models, CPU types, or cloud computing specifications. |
| Software Dependencies | No | The paper mentions software like VGG, Faster-RCNN, and Caffe, but it does not specify version numbers for these or any other software dependencies needed to replicate the experiments. |
| Experiment Setup | Yes | We set the resolution of Ie to 640 320. For the projection operator P, we map α=65.5 to W=640 pixels following SUN360 [38]... The kernel size upper bound Uk=7 7 following the max kernel size in VGG. We insert batch normalization for conv4_1 to conv5_3. |