explaingit

myrobotproject/dtof-lidar-hm-ld1-gesture-recognition

Analysis updated 2026-05-18

2PythonAudience · developerComplexity · 4/5LicenseSetup · hard

TLDR

A real-time pipeline that detects hand landmarks from a DToF LiDAR camera's depth data for contactless control.

Mindmap

mindmap
  root((HM-LD1 Gesture Recognition))
    What it does
      Depth frame capture
      Pseudocolor conversion
      Hand landmark detection
    Tech stack
      Python
      MediaPipe
      C plus plus
    Use cases
      Robot gesture control
      Smart home interaction
      JSONL landmark logging
    Audience
      Robotics developers
      Computer vision engineers

Code map

Detail Auto

An interactive map of this repo's files and how they connect — its source is parsed live in your browser. Click Visualize to build it.

filefunction / class

What do people build with it?

USE CASE 1

Add contactless hand-gesture control to a robot using a depth camera instead of a color camera.

USE CASE 2

Detect hand position and shape for a smart home device without needing good lighting.

USE CASE 3

Save hand landmark data to JSONL files for offline analysis or training other models.

USE CASE 4

Swap in a different hand landmark detection model by writing a small adapter class.

What is it built with?

PythonMediaPipeOpenCVC++CMake

How does it compare?

myrobotproject/dtof-lidar-hm-ld1-gesture-recognition0-bingwu-0/live-interpreter0cm-labs/tokenizer-benchmark
Stars222
LanguagePythonPythonPython
Setup difficultyhardmoderatemoderate
Complexity4/52/52/5
Audiencedevelopergeneralresearcher

Figures from each repo's GitHub metadata at analysis time.

How do you get it running?

Difficulty · hard Time to first run · 1h+

Requires the separate HM-LD1 camera SDK, a C++ build step, and a physical HM-LD1 DToF LiDAR camera.

The project's own code is MIT licensed, free to use, modify, and distribute, but bundled third-party SDKs and models keep their own separate licenses.

In plain English

This project builds a real time pipeline that turns depth data from a HM-LD1 DToF LiDAR camera into hand landmark detection, meaning it can track the position of a hand and its fingers using depth sensing instead of a normal color camera. It is aimed at things like contactless control for robots or smart home devices. The pipeline works in a fixed sequence: it reads small 40 by 30 depth frames from the camera, removes any depth values farther than a chosen distance (one meter by default), converts the remaining depth values into a false color image, upsamples that image to a larger 384 by 288 resolution, and then feeds it into a hand landmark detector. By default that detector is Google's MediaPipe HandLandmarker, which outputs 21 points describing the shape and position of a detected hand. Results can be viewed live with an on screen visualization or saved to a JSONL file for later use. The hand landmark detection step is kept separate from the rest of the code, so someone could swap in a different detection model by writing a small adapter class and registering it, without needing to touch the camera reading or image processing steps. Setup involves installing some system packages, running a provided script to create a Python virtual environment, downloading the MediaPipe model, and building a small C++ tool that streams frames from the camera using its official SDK, which must be obtained separately and placed next to this repository. The project has been tested on Ubuntu 22.04 and on WSL2, and includes instructions for attaching a USB camera to a WSL2 environment from Windows. Because MediaPipe was trained on ordinary color photos rather than depth based pseudocolor images, the README notes that detection quality depends on keeping the hand's depth image looking hand shaped, for example by avoiding background clutter and highly reflective or very dark surfaces. The project's own code is released under the MIT License, though the camera SDK and any third party models or packages it depends on keep their own separate licenses.

Copy-paste prompts

Prompt 1
Walk me through setting up the HM-LD1 camera pipeline and running it live on Ubuntu.
Prompt 2
Explain how the depth gate and pseudocolor conversion steps work in this pipeline.
Prompt 3
Help me attach my HM-LD1 USB camera to WSL2 so this project can read from it.
Prompt 4
Show me how to implement a custom hand landmark backend for this project.

Frequently asked questions

What is dtof-lidar-hm-ld1-gesture-recognition?

A real-time pipeline that detects hand landmarks from a DToF LiDAR camera's depth data for contactless control.

What language is dtof-lidar-hm-ld1-gesture-recognition written in?

Mainly Python. The stack also includes Python, MediaPipe, OpenCV.

What license does dtof-lidar-hm-ld1-gesture-recognition use?

The project's own code is MIT licensed, free to use, modify, and distribute, but bundled third-party SDKs and models keep their own separate licenses.

How hard is dtof-lidar-hm-ld1-gesture-recognition to set up?

Setup difficulty is rated hard, with roughly 1h+ to a first successful run.

Who is dtof-lidar-hm-ld1-gesture-recognition for?

Mainly developer.

Open on GitHub → Explain another repo

This repo across BitVibe Labs

Verify against the repo before relying on details.