task_categories:object-detection language:en license:unknown size_categories:10K<n<100K format:parquet modality:image modality:text library:datasets library:dask library:mlcroissant library:polars region:us
---
license: unknown
task_categories:
  - object-detection
language:
  - en
size_categories:
  - 10K<n<100K
dataset_info:
  features:
  - name: image
    dtype: image
  - name: description
    dtype: string
  - name: calib
    sequence:
    - name: P2
      sequence: float32
      length: 4
    - name: P3
      sequence: float32
      length: 4
    - name: R0
      sequence: float32
      length: 3
    - name: Tr_velo2cam
      sequence: float32
      length: 4
  - name: objects
    sequence:
    - name: trucation
      dtype: float64
    - name: occlusion
      dtype: float64
    - name: alpha
      dtype: float64
    - name: box2d
      sequence: float32
      length: 4
    - name: h
      dtype: float64
    - name: w
      dtype: float64
    - name: l
      dtype: float64
    - name: pos
      sequence: float32
      length: 3
    - name: ry
      dtype: float64
    - name: score
      dtype: float64
    - name: level
      dtype:
        class_label:
          names:
            '0': DontCare
            '1': Easy
            '2': Moderate
            '3': Hard
            '4': UnKnow
  - name: info
    sequence:
    - name: img_id
      dtype: int32
    - name: instance_id
      dtype: int32
    - name: ann_id
      dtype: int32
    - name: img_size
      sequence: int32
      length: 2
    - name: category
      dtype:
        class_label:
          names:
            '0': pedestrian
            '1': car
            '2': cyclist
            '3': van
            '4': truck
            '5': tram
            '6': bus
            '7': person_sitting
            '8': motorcyclist
  splits:
  - name: train
    num_examples: 29990
  - name: val
    num_examples: 5735
  - name: test
    num_examples: 5415
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: val
    path: data/val-*
  - split: test
    path: data/test-*
---

# Dataset Overview

For 3D visual grounding in monocular RGB images using language descriptions with both appearance and geometry information, 
the [Mono3DRefer](https://github.com/ZhanYang-nwpu/Mono3DVG/tree/main) contains 3D object targets with their corresponding geometric text descriptions, generated by ChatGPT and refined manually. 
Specifically, this dataset samples 2025 frames of images from the original KITTI, containing 41,140 expressions in total and a vocabulary of 5,271 words. 
The original data can be downloaded from [Google Drive](https://drive.google.com/drive/folders/1ICBv0SRbRIUnl_z8DVuH8lz7KQt580EI?usp=drive_link)

Mono3DRefer dataset splits into 29,990, 5,735, and 5,415 expressions for train/val/test sets respectively.
