pzhou10/3D-PC

---
language:
- en
license: cc-by-4.0
size_categories:
- 1K<n<10K
task_categories:
- image-classification
pretty_name: 3D perception challeng
dataset_info:
- config_name: depth
features:
- name: image
dtype: image
- name: split
dtype: string
- name: label
dtype: float64
- name: category
dtype: string
- name: scene
dtype: string
- name: setting
dtype: int64
- name: img_id
dtype: string
splits:
- name: train
num_bytes: 5116762724.6
num_examples: 3848
- name: test
num_bytes: 160973952
num_examples: 94
- name: validation
num_bytes: 632995269
num_examples: 428
download_size: 6433592072
dataset_size: 5910731945.6
- config_name: vpt-basic
features:
- name: image
dtype: image
- name: split
dtype: string
- name: label
dtype: float64
- name: category
dtype: string
- name: scene
dtype: string
- name: setting
dtype: int64
- name: img_id
dtype: string
splits:
- name: train
num_bytes: 9227531453.024
num_examples: 6568
- name: test
num_bytes: 160973952.0
num_examples: 94
- name: validation
num_bytes: 1055936900.0
num_examples: 730
download_size: 21745516242
dataset_size: 10444442305.024
- config_name: vpt-strategy
features:
- name: image
dtype: image
- name: split
dtype: float64
- name: label
dtype: int64
- name: category
dtype: string
- name: scene
dtype: string
- name: setting
dtype: int64
- name: img_id
dtype: string
splits:
- name: train
num_bytes: 149604226.0
num_examples: 100
download_size: 149610294
dataset_size: 149604226.0
configs:
- config_name: depth
data_files:
- split: train
path: depth/train-*
- split: test
path: depth/test-*
- split: validation
path: depth/validation-*
- config_name: vpt-basic
data_files:
- split: train
path: vpt-basic/train-*
- split: test
path: vpt-basic/test-*
- split: validation
path: vpt-basic/validation-*
- config_name: vpt-strategy
data_files:
- split: train
path: vpt-strategy/train-*
---
## Project Description
- **arXiv:** https://arxiv.org/abs/2406.04138
- **GitHub Repo:** https://github.com/serre-lab/VPT
- **Project Page:** https://serre-lab.github.io/VPT

## Summary
Visual perspective taking (VPT), the ability to accurately perceive and reason about the perspectives of others, is an essential feature of human intelligence.
Deep neural networks (DNNs) may be a good candidate for modeling VPT and its computational demands in light of a growing number of reports indicating that DNNs gain the ability to analyze 3D scenes after training on large static-image datasets.
We developed the 3D perception challenge (3D-PC) for comparing 3D perceptual capabilities in humans and DNNs.
The 3D-PC is comprised of three 3D-analysis tasks posed within natural scene images:
1. A simple test of object depth order (depth),
2. A basic VPT task (vpt-basic),
3. A version of VPT (vpt-strategy) designed to limit the effectiveness of "shortcut" visual strategies.

## Citation
``` latex
@misc{linsley20243dpc,
title={The 3D-PC: a benchmark for visual perspective taking in humans and machines},
author={Drew Linsley and Peisen Zhou and Alekh Karkada Ashok and Akash Nagaraj and Gaurav Gaonkar and Francis E Lewis and Zygmunt Pizlo and Thomas Serre},
year={2024},
eprint={2406.04138},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```

原始信息汇总

数据集概述

基本信息

• 语言：英语
• 许可证：CC-BY-4.0
• 大小分类：1K<n<10K
• 任务分类：图像分类
• 美观名称：3D感知挑战

数据集配置

1. 配置名称：depth

   • 特征：
     • image: 图像
     • split: 字符串
     • label: 浮点数
     • category: 字符串
     • scene: 字符串
     • setting: 整数
     • img_id: 字符串
   • 分割：
     • train: 3848个样本，5116762724.6字节
     • test: 94个样本，160973952字节
     • validation: 428个样本，632995269字节
   • 下载大小：6433592072字节
   • 数据集大小：5910731945.6字节

2. 配置名称：vpt-basic

   • 特征：
     • image: 图像
     • split: 字符串
     • label: 浮点数
     • category: 字符串
     • scene: 字符串
     • setting: 整数
     • img_id: 字符串
   • 分割：
     • train: 6568个样本，9227531453.024字节
     • test: 94个样本，160973952字节
     • validation: 730个样本，1055936900.0字节
   • 下载大小：21745516242字节
   • 数据集大小：10444442305.024字节

3. 配置名称：vpt-strategy

   • 特征：
     • image: 图像
     • split: 浮点数
     • label: 整数
     • category: 字符串
     • scene: 字符串
     • setting: 整数
     • img_id: 字符串
   • 分割：
     • train: 100个样本，149604226.0字节
   • 下载大小：149610294字节
   • 数据集大小：149604226.0字节

数据文件

• depth：
  • train: depth/train-*
  • test: depth/test-*
  • validation: depth/validation-*
• vpt-basic：
  • train: vpt-basic/train-*
  • test: vpt-basic/test-*
  • validation: vpt-basic/validation-*
• vpt-strategy：
  • train: vpt-strategy/train-*