The PCD (Point Cloud Data) file format

This document describes the PCD (Point Cloud Data) file format, and the way it is used inside Point Cloud Library (PCL).

PCD file format icon

Why a new file format?

The PCD file format is not meant to reinvent the wheel, but rather to complement existing file formats that for one reason or another did not/do not support some of the extensions that PCL brings to n-D point cloud processing.

PCD is not the first file type to support 3D point cloud data. The computer graphics and computational geometry communities in particular, have created numerous formats to describe arbitrary polygons and point clouds acquired using laser scanners. Some of these formats include:

  • PLY - a polygon file format, developed at Stanford University by Turk et al

  • STL - a file format native to the stereolithography CAD software created by 3D Systems

  • OBJ - a geometry definition file format first developed by Wavefront Technologies

  • X3D - the ISO standard XML-based file format for representing 3D computer graphics data

  • and many others

All the above file formats suffer from several shortcomings, as explained in the next sections – which is natural, as they were created for a different purpose and at different times, before today’s sensing technologies and algorithms had been invented.

PCD versions

PCD file formats might have different revision numbers, prior to the release of Point Cloud Library (PCL) version 1.0. These are numbered with PCD_Vx (e.g., PCD_V5, PCD_V6, PCD_V7, etc) and represent version numbers 0.x for the PCD file.

The official entry point for the PCD file format in PCL however should be version 0.7 (PCD_V7).

File format header

Each PCD file contains a header that identifies and declares certain properties of the point cloud data stored in the file. The header of a PCD must be encoded in ASCII.

Note

Each header entry as well as ascii point data (see below) specified in a PCD file, is separated using new lines (\n).

As of version 0.7, the PCD header contains the following entries:

  • VERSION - specifies the PCD file version

  • FIELDS - specifies the name of each dimension/field that a point can have. Examples:

    FIELDS x y z                                # XYZ data
FIELDS x y z rgb                            # XYZ + colors
FIELDS x y z normal_x normal_y normal_z     # XYZ + surface normals
FIELDS j1 j2 j3                             # moment invariants
...

  • SIZE - specifies the size of each dimension in bytes. Examples:

    • unsigned char/char has 1 byte

    • unsigned short/short has 2 bytes

    • unsigned int/int/float has 4 bytes

    • double has 8 bytes

  • TYPE - specifies the type of each dimension as a char. The current accepted types are:

    • I - represents signed types int8 (char), int16 (short), and int32 (int)

    • U - represents unsigned types uint8 (unsigned char), uint16 (unsigned short), uint32 (unsigned int)

    • F - represents float types

  • COUNT - specifies how many elements does each dimension have. For example, x data usually has 1 element, but a feature descriptor like the VFH has 308. Basically this is a way to introduce n-D histogram descriptors at each point, and treating them as a single contiguous block of memory. By default, if COUNT is not present, all dimensions’ count is set to 1.

  • WIDTH - specifies the width of the point cloud dataset in the number of points. WIDTH has two meanings:

    • it can specify the total number of points in the cloud (equal with POINTS see below) for unorganized datasets;

    • it can specify the width (total number of points in a row) of an organized point cloud dataset.

    Also see HEIGHT.

    Note

    An organized point cloud dataset is the name given to point clouds that resemble an organized image (or matrix) like structure, where the data is split into rows and columns. Examples of such point clouds include data coming from stereo cameras or Time Of Flight cameras. The advantages of a organized dataset is that by knowing the relationship between adjacent points (e.g. pixels), nearest neighbor operations are much more efficient, thus speeding up the computation and lowering the costs of certain algorithms in PCL.

    Examples:

    WIDTH 640     # there are 640 points per line

  • HEIGHT - specifies the height of the point cloud dataset in the number of points. HEIGHT has two meanings:

    • it can specify the height (total number of rows) of an organized point cloud dataset;

    • it is set to 1 for unorganized datasets (thus used to check whether a dataset is organized or not).

    Example:

    WIDTH 640       # Image-like organized structure, with 480 rows and 640 columns,
HEIGHT 480      # thus 640*480=307200 points total in the dataset

    Example:

    WIDTH 307200
HEIGHT 1        # unorganized point cloud dataset with 307200 points

  • VIEWPOINT - specifies an acquisition viewpoint for the points in the dataset. This could potentially be later on used for building transforms between different coordinate systems, or for aiding with features such as surface normals, that need a consistent orientation.

    The viewpoint information is specified as a translation (tx ty tz) + quaternion (qw qx qy qz). The default value is:

    VIEWPOINT 0 0 0 1 0 0 0

  • POINTS - specifies the total number of points in the cloud. As of version 0.7, its purpose is a bit redundant, so we’re expecting this to be removed in future versions.

    Example:

    POINTS 307200   # the total number of points in the cloud

  • DATA - specifies the data type that the point cloud data is stored in. As of version 0.7, three data types are supported: ascii, binary, and binary_compressed. See the next section for more details.

Note

The next bytes directly after the header’s last line (DATA) are considered part of the point cloud data, and will be interpreted as such.

Warning

The header entries must be specified precisely in the above order, that is:

VERSION
FIELDS
SIZE
TYPE
COUNT
WIDTH
HEIGHT
VIEWPOINT
POINTS
DATA

Data storage types

As of version 0.7, the .PCD file format uses three different modes for storing data:

  • in ASCII form, with each point on a new line:

    p_1
p_2
p_3
p_4
...

p_n

Note

Starting with PCL version 1.0.1 the string representation for NaN is “nan”.

  • in binary form, where the data is a complete memory copy of the pcl::PointCloud.points array/vector. On Linux systems, we use mmap/munmap operations for the fastest possible read/write access to the data.

  • in binary_compressed form. The body (everything after the header) starts with a 32 bit unsigned binary number which specifies the size in bytes of the data in compressed form. Next is another 32 bit unsigned binary number which specifies the size in bytes of the data in uncompressed form. Then follows the compressed data. The compression and decompression is done using Marc Lehmann’s LZF algorithm. It is mediocre in terms of size reduction, but very fast. For typical point clouds, the compressed data has 30 to 60 percent of the original size. Before compressing, the data is reordered to improve compression, from the standard array-of-structures layout to a structure-of-arrays layout. So for example a cloud with three points and fields x, y, z would be reordered from xyzxyzxyz to xxxyyyzzz.

Storing point cloud data in both a simple ascii form with each point on a line, space or tab separated, without any other characters on it, as well as in a binary dump format, allows us to have the best of both worlds: simplicity and speed, depending on the underlying application. The ascii format allows users to open up point cloud files and plot them using standard software tools like gnuplot or manipulate them using tools like sed, awk, etc.

Advantages over other file formats

Having PCD as (yet another) file format can be seen as PCL suffering from the not invented here syndrome. In reality, this is not the case, as none of the above mentioned file formats offers the flexibility and speed of PCD files. Some of the clearly stated advantages include:

  • the ability to store and process organized point cloud datasets – this is of extreme importance for real time applications, and research areas such as augmented reality, robotics, etc;

  • binary mmap/munmap data types are the fastest possible way of loading and saving data to disk.

  • storing different data types (all primitives supported: char, short, int, float, double) allows the point cloud data to be flexible and efficient with respect to storage and processing. Invalid point dimensions are usually stored as NAN types.

  • n-D histograms for feature descriptors – very important for 3D perception/computer vision applications

An additional advantage is that by controlling the file format, we can best adapt it to PCL, and thus obtain the highest performance with respect to PCL applications, rather than adapting a different file format to PCL as the native type and inducing additional delays through conversion functions.

Note

Though PCD (Point Cloud Data) is the native file format in PCL, the pcl_io library should offer the possibility to save and load data in all the other aforementioned file formats too.

Example

A snippet of a PCD file is attached below. It is left to the reader to interpret the data and see what it means. :) Have fun!:

# .PCD v.7 - Point Cloud Data file format
VERSION .7
FIELDS x y z rgb
SIZE 4 4 4 4
TYPE F F F F
COUNT 1 1 1 1
WIDTH 213
HEIGHT 1
VIEWPOINT 0 0 0 1 0 0 0
POINTS 213
DATA ascii
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