Datasets:

RAY-AUTRA-TECHNOLOGY
/

img_pointV2

license: apache-2.0
tags:
  - vision
  - point cloud
  - NYU Depth V2
  - 3d
  - deep learning
  - computer vision
  - RAY-AUTRA-TECHNOLOGY
language: en
pretty_name: RAY-tech img_pointV2
datasets:
  - jagennath-hari/nyuv2

img_pointV2 is available 🎉🎉🎉🥳🥳😀😀

This dataset is a collection of 3D point clouds generated from the jagennath-hari/nyuv2dataset.

img_pointV2 is the second version of the RAY-AUTRA-TECHNOLOGY/img_pointV dataset. It is a spatialized version of the NYU Depth V2 dataset, transforming classic indoor images into high-fidelity 3D point clouds (.ply files).

The main objective is to provide clean, ready-to-use 3D scenes for training 3D vision models, eliminating the need for users to manually handle RGB-D to point cloud conversion.

Dataset Highlights

Point Clouds (.ply): Complete 3D scenes featuring both geometry ($X, Y, Z$) and color ($R, G, B$).
Metric Precision: Every point is accurately positioned in meters, strictly following the real-world Kinect camera intrinsic parameters.
Cleaned & Uniformed: Clouds have been filtered to remove capture noise and voxelized with a 1 cm density (voxel size: $0.01$).
Integrated Labels: Metadata preserves all original semantic and instance segmentation information.

File Structure

File/Folder	Description
`data/`	Directory containing the `.ply` files.
`metadata.arrow`	Central index linking IDs, filenames, and point counts (Train/Val/Test splits).
`camera_params.json`	Optical parameters (intrinsics) used for the 3D reconstruction.
`class_names.json`	Dictionary of semantic classes (e.g., chair, wall, table).
`config.yaml`	Dataset configuration (license, format, normalization).

IMPORTANT: These files are fully compatible with major 3D libraries such as Open3D, PyTorch Geometric, and PointNet++.

RAY AUTRA TECHNOLOGY 2025