copc_converter 0.1.1

Fast, memory-efficient converter from LAS/LAZ to COPC (Cloud-Optimized Point Cloud)
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copc_converter

Crates.io

A fast, memory-efficient converter that turns LAS/LAZ point cloud files into COPC (Cloud-Optimized Point Cloud) files.

Features

  • Produces spec-compliant COPC 1.0 files (LAS 1.4, point format 7)
  • Merges multiple input files into a single COPC output
  • Out-of-core processing with a configurable memory budget — handles datasets larger than RAM
  • Parallel reading, octree construction, and LAZ compression via rayon
  • Preserves WKT CRS from input files

Installation

Requires Rust 1.85+.

From crates.io

cargo install copc_converter

From source

git clone https://github.com/360-geo/copc-converter.git
cd copc-converter
cargo install --path .

This installs the copc_converter binary to ~/.cargo/bin/, which should be on your PATH.

Pre-built binaries

Download pre-built binaries from the GitHub releases page. These are built for broad compatibility and run on any machine.

For best performance, prefer installing from source via cargo install — this automatically compiles with target-cpu=native, optimizing for your specific CPU's instruction set (AVX2, NEON, etc.).

Usage

# Single file
copc_converter input.laz -o output.copc.laz

# Multiple files
copc_converter tile1.laz tile2.laz tile3.laz -o merged.copc.laz

# Directory of LAZ/LAS files
copc_converter ./tiles/ -o merged.copc.laz

Options

Flag Description Default
-o, --output Output COPC file path (required)
--memory-limit Max memory budget (16G, 4096M, etc.) 16G
--temp-dir Directory for intermediate files system temp

Example

copc_converter ./my_survey/ -o survey.copc.laz --memory-limit 8G

How it works

  1. Scan — reads headers from all input files in parallel to determine bounds, CRS, and point count.
  2. Distribute — reads every point, assigns it to an octree leaf voxel, and writes it to a temporary file on disk.
  3. Build — constructs the octree bottom-up, thinning points at each level to produce multi-resolution LODs.
  4. Write — encodes and compresses nodes in parallel into a single COPC file with a hierarchy EVLR for spatial indexing.

License

MIT