Steuart Systems

The cameras are finally calibrated, and the communications and power systems are installed and working. Now I can finally begin producing scans to test and fine tune the software.

Today I scanned part of the lab, and the animated GIF illustrates the 3D nature of the scan. When producing a 3D model, multiple perspectives must be captured to fill in occlusions (blind spots). For this model, three scans from different locations were merged to produce a point cloud. The GIF consists of 7 different screen-shots of the point-cloud. While there are still occlusions, many have been filled. For example, notice that you can see both above and below the table.

The original 32-bit software that we use to turn pictures into 3D models is almost 5 years old, and it runs on 32-bit Windows XP. The old software often crashes when processing high resolution images because the 2GB memory limit isn’t enough to process the gigabytes of data that our scanner can quickly produce. Today’s scan was made on a computer running 64-bit Windows 7, and we are currently replacing the old 32-bits software with more advanced 64-bit code. The new software runs much faster in 64-bit mode because it can keep temporary files in RAM instead of writing them to and reading them from a slow disk. Even using a Solid State Drive (SSD) wastes minutes of unnecessary processing.

COMING UP: Much better scans processed by SketchUp & posted into Google Earth.

Four buildings that we have recently uploaded

We can now create 3D buildings and place them into Google Earth. The results can be viewed with a normal web browser, and we are exploring how to take advantage of this new low cost form of web promotion. Creating 3D models for Google Earth is a useful capability that we intend to continue refining, and we will begin offering the service of modeling local buildings and placing them into Google Earth. Click here to go to Purcellville Virginia and see one of our 3D model on Google Maps. Use the left, right, and center mouse buttons for navigation.

Here is an overview of how to put images onto Google Earth. The program Google SketchUp can be used to create photorealistic 3D models of real buildings. Once a model is built it can be uploaded to Google for possible placement into Google Earth. A model is integrated into Google Earth only after a reviewer decides that it satisfies all of Google’s acceptance criteria. Our early efforts were rejected for reasons like being too big, being too complex, or for exhibiting “Z-fighting.” At times we have experienced delays of over four weeks during the review process, but after nearly three months of effort we have learned how to efficiently make models that Google will accept and place into Google Earth.

While creating 3D content for Google Earth is interesting, our main objective is to build a practical 3D-360 photorealistic scanner. Prototype-4E is in the final stages of assembly, and this July we plan to begin using it to create photorealistic interiors for our 3D models. When the results are good enough we plan to use SketchUp and Google Earth to demonstrate the ability to create models that you can walk around and also into. Once inside you will see detailed photorealistic interiors that are too complex to model with most traditional techniques.

Adrian and I have started investigating how to merge the 3D-360 output into Google SketchUp, and ultimately into Google Earth. This April we expect to expand his latest 3D model of porch into 3D models of complete house. Once the house is complete will photorealistic interior. The interior will produced by scanning real rooms within Prototype 4E of 3D-360.

Adrian’s portfolio of SketchUp models can found here.

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The Prototype-4.x family of 3D-360s is based on a camera that we have been developing for over a year.  While several areas of enhancement are still left to be implemented, the new camera is ready to be compared against the Canon 5D.  Prototype-3 used eight Canon 5Ds, and the new camera in Prototype-4 needs to meet or exceed the 5D’s performance.

One significant difference between our camera and the Canon 5D is that the 5D (and all other color cameras) uses tiny color filters arranged in a Bayer pattern on top of the individual pixels inside of the camera.  While the 5D has 12 million pixels, only 3 million are RED, 6 million are GREEN, and 3 million are BLUE.  Our camera is arguably a 15 million pixel sensor because it cycles through three large filters with the 5 million pixel monochrome sensor to produce 5 million RED pixels, 5 million GREEN pixels, and 5 million BLUE pixels. Our camera is immune to color artifacts caused by the Bayer patterns, but taking a picture takes three times longer because the filters must be rotated into place between shots. Fortunately our system automatically changes between filters in less than one second.  In the future we may want to add filters for other parts of the spectrum including infrared (IR) and ultra violet.

The purpose of this test is to compare the color reproduction, noise, and Bayer pattern artifacts between the two cameras. The 5D has a 14mm Canon lens, and the FOV is similar to our custom lens. Here is the test procedure:

1) Take a picture with each camera in RAW mode

2) Use minimal automatic processing on each image.  For the 3D-360 Photoshop was used for color balance and sharpening.  For the Canon 5D the image was processed with DxO

3) Compare the cropped images at actual size and zoomed to 600%

Here are the results:

Above is the shot from the Prototype-4 camera,

And below is the shot from the Canon 5D.

The two shots show that our camera compares well to the Canon 5D.  A slight BLUE halo is visible to the left of some objects, but this may be caused by a dirty or warped Wratten filter.

Below is a zoomed comparison of the areas the GREEN circles.

Close inspection shows that the 3D-360 camera has less noise and fewer Bayer pattern artifacts, but the 5D seems a little sharper.  The difference in sharpness could be related to the dynamic range of the two images.  The raw 3D-360 image covers a linear range of 24 bits, but the 5D covers a smaller range of only 12 bits.  We use a combination of linear and logarithmic curves to squeeze the 24 bits per pixel per color channel down to 16 bits per pixel per channel.  To improve contrast we may reduce our range from 24 bits to 22 bits.

I am pleased with this early test, and we are currently implementing upgrades that should make the difference even more dramatic.

This is the first color image produced by the new camera & lens combination. The bilinear rectification routine that we completed last week was automatically applied to correct chromatic aberration.  In the future bicubic interpolation will make the image even sharper.  The original 16-bit image had levels and curves adjusted in Photoshop, and the result was converted to the 8-bit JPG below.