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{ "class": [ 62, 22, 67, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49 ], "bbox": [ [ 402.6099853515625, 240.52999877929688, 412.1600036621094, 248.83999633789062 ], [ 511.1000061035156, 254.83999633789062, 547.5200...
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{ "class": [ 41, 17 ], "bbox": [ [ 179.02000427246094, 318.8999938964844, 237.16000366210938, 408 ], [ 151.27999877929688, 23.84000015258789, 519.8599853515625, 359.4100036621094 ] ], "depth": [ 0.7833401560783386, 0.8366194367408...
{ "class": [ 6 ], "bbox": [ [ 201.77000427246094, 86.61000061035156, 494.0899963378906, 353.3399963378906 ] ], "depth": [ 4.063345432281494 ], "orientation": [ { "azimuth": 314, "polar": -12, "rotation": 0, "confidence": 0.995243728160858...
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COCO 3D 扩展数据集

数据集描述

本数据集基于 COCO 2017 训练集,添加了深度和方向信息:

  • 深度:通过 depth-pro 模型预测的绝对深度(米)。
  • 方向:通过 orient-anything 模型预测的方位角(azimuth/polar)。
  • 原始标注:包括类别、2D 框、RLE 分割掩码。

使用示例

from datasets import load_dataset
dataset = load_dataset("lidaiqiang/COCO_3D", split="train")
sample = dataset[0]
print(sample["objects"][0]["depth"])  # 打印第一个物体的深度

字段说明

字段名 类型 描述
image Image PIL 格式的原始图像
objects Sequence 物体标注列表(见下表)
class str 物体类别(如 "cat")
bbox [float32] 2D 边界框 [x1, y1, x2, y2]
depth float32 绝对深度值(米)

License

COCO 原始 License(Apache 2.0)。


交互式查看器

📊 点击展开数据表格(实时解析)

物体详情

类别: -

边界框: -

深度: -

方向: -

ID 类别 边界框
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