PointCloudLibrary / PointCloudLibrary/pcl

[octree] "Using more than one type of container in octree construction"

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kind: request status: stale status: triage
Dominant language
C++
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Description

Multiple type of octree's and octree's container are implemented, but anyone cover all need of my problem.

Context
I am doing a project that involves use of PCA in all the points inside each leaf voxel of the tree. After that, I have to seach in the neighboor leaf voxels to find some similarity.

Expected behavior

The expected behavior is to use only one type of octree and do both operations, PCA and Voxel Search.

Current Behavior

Even using to types of octree, the trees are not exactly the same. So, with the structures that PCL have implemented i can do just one of the operations.

Describe the solution you'd like

An explanation of what i can do to solve the problem, using the PCL structures or even implementing my one.

Describe alternatives you've considered

I've considered to implement my own octree extending OctreePointCloud, but I need an explanation before do this step.

Thanks.

Contributor guide

Open the contributing guide

First steps

  1. Read the whole issue, then the project's contributing guide.
  2. Comment on the issue to say you are picking it up — it saves two people doing the same work.
  3. Fork the repository and make your change on a branch.
  4. Open a pull request that references the issue number.

Research direction

Start by comparing the PCL octree and container structures mentioned in the issue, especially OctreePointCloud, and trace how each supports PCA over leaf voxels versus neighboring-voxel search. Determine whether one existing structure can support both operations or whether the request requires a new design. Done means providing a documented, technically supported approach with clear limitations.

Written by the indexing model from the issue text.

Assessment

Tech stack
cpp
Domain
computer-vision
Issue type
Feature
Difficulty
5/5
Estimated time
Over a week
Activity status
Stale
Clarity
Needs clarification
Newbie friendliness
25/100

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