When the Raspberry Pi 4 came out, [Frank Zhao] saw the potential to make a realtime 3D scanner that was completely handheld and self-contained. The device has an Intel RealSense D415 depth-sensing camera as the main sensor, which uses two IR cameras and an RGB camera along with the Raspberry Pi 4. The Pi uses a piece of software called RTAB-Map — intended for robotic applications — to take care of using the data from the camera to map the environment in 3D and localize itself within that 3D space. Everything gets recorded in realtime.
This handheld device can act as a 3D scanner because the data gathered by RTAB-Map consists of a point cloud of an area as well as depth information. When combined with the origin of the sensing unit (i.e. the location of the camera within that area) it can export a point cloud into a mesh and even apply a texture derived from the camera footage. An example is shown below the break. Continue reading “Handheld 3D Scanning, Using Raspberry Pi 4 And Intel RealSense Camera”→
It’s sad that nearly half a century after the achievements of the Apollo program we’re still arguing with a certain subset of people who insist it never happened. Poring through the historical record looking for evidence that proves the missions couldn’t possibly have occurred has become a sad little cottage industry, and debunking the deniers is a distasteful but necessary ongoing effort.
One particularly desperate denier theory holds that fully spacesuited astronauts could never have exited the tiny hatch of the Lunar Excursion Module (LEM). [AstronomyLive] fought back at this tendentious claim in a clever way — with a DIY LIDAR scanner to measure Apollo artifacts in museums. The hardware is straightforward, with a Garmin LIDAR-Lite V3 scanner mounted on a couple of servos to make a quick pan-tilt head. The rig has a decidedly compliant look to it, with the sensor flopping around a bit as the servos move. But for the purpose, it seems perfectly fine.
[AstronomyLive] took the scanner to two separate museum exhibits, one to scan a LEM hatch and one to scan the suit Gene Cernan, the last man to stand on the Moon so far, wore while training for Apollo 17. With the LEM flying from the rafters, the scanner was somewhat stretching its abilities, so the point clouds he captured were a little on the low-res side. But in the end, a virtual Cernan was able to transition through the virtual LEM hatch, as expected.
Sadly, such evidence will only ever be convincing to those who need no convincing; the willfully ignorant will always find ways to justify their position. So let’s just celebrate the achievements of Apollo.
If you ever watch the original Star Trek, Captain Kirk and crew spend a lot of time mapping new parts of the galaxy. In fact, at least one episode centered on them taking images of some new part of space. It might not be new, but if you have a drone, you probably have accumulated a lot of frames of aerial imagery from around your house (or wherever you fly).
WebODM allows you to create georeferenced maps, point clouds and textured 3D models from your drone footage. The software is really an integration and workflow manager for Open Drone Map, which does most of the heavy lifting.
This modification to real objects begin with [Greg] taking dozens of pictures of the target object at many different angles. These pictures are then imported into Agisoft PhotoScan which takes all these photos and converts it into a very high-resolution, full-color point cloud.
After precisely measuring the real-world dimensions of the object to be modeled, [Greg] imported his point cloud into Blender and got started on the actual 3D modeling task. By reconstructing the original sandstone block in Blender, [Greg] was also able to model Lego parts.After subtracting the part of the model above the Lego parts, [Greg] had a bizarre-looking adapter that adapts Lego pieces to a real-life stone block.
It’s a very, very cool projet that demonstrates how good [Greg] is at making 3D models of real objects and modeling them inside a computer. After the break you can see a walkthrough of his work process, an impressive amount of expertise wrapped up in making the world just a little more strange.
[James]’ project focuses on the problem of modeling mixing liquids from a multi-camera setup. The hardware is fairly basic, just 16 consumer-level video cameras arranged in a semicircle around a glass beaker full of water.
When [James] injects a little dye into the water, the diffusing cloud is captured by a handful of Sony camcorders. The images from these camcorders are sent through an algorithm that selects one point in the cloud and performs a random walk to find every other point in the cloud of liquid dye.
The result of all this computation is a literal volumetric cloud, allowing [James] to render, slice, and cut the cloud of dye any way he chooses. You can see the videos produced from this very cool build after the break.