PCL VS colmap

Did you read the https://github.com/PointCloudLibrary/pcl/blob/master/test/fuzz/build.sh

If you need some specific UI, you could choose a UI library which are better for your needs, eg. link1, link2 or link3.

Point Cloud Library files (PCD) 2

Point Cloud Library has a bunch of 3D features to choose from. They're for unstructured point clouds but I think they should also work for volumetric data by converting it to point clouds in a 3D grid or something similar.

Hello! I am looking for ways to process geometric data (mainly point clouds). I am familiar with Point cloud Library (PCL) and Point Data Abstraction Library (PDAL) in C++ but can't seem to find an equivalent crate in Rust (Pasture seems to be experimental). Are there any stable/robust alternatives at the moment?

If the point clouds are roughly aligned after applying their global pose, then usually ICP (iterated closest point) is what you want. There is probably what you need in PCL: https://pointclouds.org/

If you're using python then you can use something like https://github.com/daavoo/pyntcloud to manipulate / render from different angles. If you're using c++ try https://pointclouds.org/

I am currently working on a project that uses PCL. PCL supports CUDA (which I require for my project), however I can't seem to figure out how to build / install PCL with CUDA support. I looked at the PCL default.nix, and it seems that it can be built with CUDA support if the "cudatoolkit" package is installed. However, when I add PCL to the buildInputs in my project's shell.nix, it downloads a version of PCL that doesn't have CUDA support and my project fails to build.

Are you aware of the tutorials on https://pointclouds.org ?

Not saying its easy to use, but there is an application gui and it is free: https://github.com/colmap/colmap

Colmap (Model folders (BIN and TXT), dense workspaces, NVM, PLY)

See colmap for example https://colmap.github.io/

You're looking into a problem called photogrammetry, and a well-studied one at that. I'd recommend looking into "shape from motion" (sfm); specifically techniques that do "dense reconstruction." I'd recommend COLMAP to start with. It does pose estimation from images (e.g. you point it at a bunch of images and it will figure out the relative poses of the cameras that took them), as well as sparse and dense reconstcution.

COLMAP builds dense meshes from a collection of cameras https://colmap.github.io/

I’ve seen references to using COLMAP (https://colmap.github.io/) to estimate camera position/pose, e.g. here

Classical photogrammetry, where I agree with u/tdgros that the way to go is https://colmap.github.io/. There are actually better variants in literature but nothing is more reliable and user-friendly than COLMAP. This will give you a very precise point cloud, that can be meshed if needed.