February 5, 2018

High Resolution Multi-View Stereo: Tile Processor and Convolutional Neural Network

by Andrey Filippov

Figure 1. Multi-board setup for the TP+CNN prototype

This article describes our next steps that will continue the year-long research on high resolution multi-view stereo for long distance ranging and 3-D reconstruction. We plan to fuse the methods of high resolution images calibration and processing, already emulated functionality of the Tile Processor (TP), RTL code developed for its implementation and the Convolutional Neural Network (CNN). Compared to the CNN alone this approach promises over a hundred times reduction in the number of input features without sacrificing universality of the end-to-end processing. The TP part of the system is responsible for the high resolution aspects of the image acquisition (such as optical aberrations correction and image rectification), preserves deep sub-pixel super-resolution using efficient implementation of the 2-D linear transforms. Tile processor is free of any training, only a few hyperparameters define its operation, all the application-specific processing and “decision making” is delegated to the CNN.

(more…)

January 8, 2018

Efficient Complex Lapped Transform Implementation for the Space-Variant Frequency Domain Calculations of the Bayer Mosaic Color Images

by Andrey Filippov

This post continues discussion of the small tile space-variant frequency domain (FD) image processing in the camera, it demonstrates that modulated complex lapped transform (MCLT) of the Bayer mosaic color images requires almost 3 times less computational resources than that of the full RGB color data.

“Small Tile” and “Space Variant”

Why “small tile“? Most camera images have short (up to few pixels) correlation/mutual information span related to the acquisition system properties – optical aberrations cause a single scene object point influence a small area of the sensor pixels. When matching multiple images increase of the window size reduces the lateral (x,y) resolution, so many of the 3d reconstruction algorithms do not use any windows at all, and process every pixel individually. Other limitation on the window size comes from the fact that FD conversions (Fourier and similar) in Cartesian coordinates are shift-invariant, but are sensitive to scale and rotation mismatch. So targeting say 0.1 pixel disparity accuracy the scale mismatch should not cause error accumulation over window width exceeding that value. With 8×8 tiles (16×16 overlapped) acceptable scale mismatch (such as focal length variations) should be under 1%. That tolerance is reasonable, but it can not get much tighter.

What is “space variant“? One of the most universal operations performed in the FD is convolution (also related to correlation) that exploits convolution-multiplication property. Mathematically convolution applies the same operation to each of the points of the source data, so shifted object of the source image produces just a shifted result after convolution. In the physical world it is a close approximation, but not an exact one. Stars imaged by a telescope may have sharper images in the center, but more blurred in the peripheral areas. While close (angularly) stars produce almost the same shape images, the far ones do not. This does not invalidate convolution approach completely, but requires kernel to (smoothly) vary over the input images [12], makes it a space-variant kernel.

Figure 1. Complex Lapped Transform with DCT-IV/DST-IV: time-domain aliasing cancellation (TDAC) property. a) selection of overlapping input subsequences 2*N-long, multiplication by sine window; b) creating N-long sequences for DCT-IV (left) and DST-IV (right); c) (after frequency domain processing) extending N-long sequence using DCT-IV boundary conditions (DST-IV processing is similar); d) second multiplication by sine window; e) combining partial data

There is another issue related to the space-variant kernels. Fractional pixel shifts are required for multiple steps of the processing: aberration correction (obvious in the case of the lateral chromatic aberration), image rectification before matching that accounts for lens optical distortion, camera orientation mismatch and epipolar geometry transformations. Traditionally it is handled by the image rectification that involves re-sampling of the pixel values for a new grid using some type of the interpolation. This process distorts the signal data and introduces non-linear errors that reduce accuracy of the correlation, that is important for subpixel disparity measurements. Our approach completely eliminates resampling and combines integer pixel shift in the pixel domain and delegates the residual fractional pixel shift (±0.5 pix) to the FD, where it is implemented as a cosine/sine phase rotator. Multiple sources of the required pixel shift are combined for each tile, and then a single phase rotation is performed as a last step of pixel domain to FD conversion.

Frequency Domain Conversion with the Modulated Complex Lapped Transform

Modulated Complex Lapped Transform (MCLT)[3] can be used to split input sequence into overlapping fractions, processed separately and then recombined without block artifacts. Popular application is the signal compression where “processed separately” means compressed by the encoder (may be lossy) and then reconstructed by the decoder. MCLT is similar to the MDCT that is implemented with DCT-IV, but it additionally preserves and allows frequency domain modification of the signal phase. This feature is required for our application (fractional pixel shifts and asymmetrical lens aberrations modify phase), and MCLT includes both MDCT and MDST (that use DCT-IV and DST-IV respectively). For the image processing (2d conversion) four sub-transforms are needed:

  • horizontal DCT-IV followed by vertical DCT-IV
  • horizontal DST-IV followed by vertical DCT-IV
  • horizontal DCT-IV followed by vertical DST-IV
  • horizontal DST-IV followed by vertical DST-IV

(more…)

December 13, 2017

Assembling Long Range Stereo Camera

by olga

MNC393-XCAM parts

MNC393-XCAM partial assembly and parts

The long anticipated parts for the Long range camera have arrived! The mechanical parts for the MNC393-XCAM – Long Range Multi-view Stereo Camera are machined, tested, and ready to be anodized. This enables us to have the X-camera assembled before the winter holidays. The holiday break will provide a good opportunity to test the camera, capture new photos, and create robust 3D models from calibrated images. The titanium X-frame of the camera ensures thermal stability required for continuous accuracy of 3D measurements. The aluminum enclosure and sealed lens filters weatherproof the system allowing for the proposed outdoor use of the camera. We intend to assemble two cameras: one with a 150 mm distance between the sensors and another with a longer baseline. The expected accuracy for the camera with the shorter baseline is greater than 10% at a 200 meter distance. We have achieved 10% accuracy with H-camera with calibrated sensors, even though the 3D-printed parts were not thermally stable and some error was accumulated over time. It was a very pleasant surprise that the software was still able to deal with somewhat un-calibrated images and detect distances very accurately, creating impressive 3D-scenes: Scene_viewer The second camera will have a 280 mm distance between sensors, which is determined by the longest FPC cables we can use without signal losses. It promises to double the measured distance with the same degree of accuracy, therefore an extremely long range 3D-scenes will be produced. The Long Range Multi-View Stereo Camera with 4 sensors MNC393-XCAM is planned for release in early 2018.

September 25, 2017

Natural environments in 3D with Elphel camera and Blender

by Paulina Filippova and Fyodor Filippov

Setting 3D camera on the rock at Cape Alava

Testing 3D camera

on a road trip

In August of 2017, my family and I went on a trip to the Pacific Northwest, partially for a much needed vacation, but equally as importantly, to test my dad’s new 3D camera. My dad had been designing calibrated multi-sensor cameras for as long as I can remember, and since February was working determinately on developing principally new algorithms for reconstructing a 3D model from a set of 4 simultaneously taken photographs . Now that the camera and the software were ready, there was no better time to test it. (more…)

September 20, 2017

Long range multi-view stereo camera with 4 sensors

by Andrey Filippov

Figure 1. Four sensor stereo camera

Four-camera stereo rig prototype is capable of measuring distances thousands times exceeding the camera baseline over wide (60 by 45 degrees) field of view. With 150 mm distance between lenses it provides ranging data at 200 meters with 10% accuracy, production units will have higher accuracy. Initial implementation uses software post-processing, but the core part of the software (tile processor) is designed as FPGA simulation and will be moved to the actual FPGA of the camera for the real time applications. Scroll down or just hyper-jump to Scene viewer for the links to see example images and reconstructed scenes. (more…)

May 10, 2016

3D Print Your Camera Freedom

by Andrey Filippov

Two weeks ago we were making photos of our first production NC393 camera to post an announcement of the new product availability. We got all the mechanical parts and most of the electronic boards (14MPix version will be available shortly) and put them together. Nice looking camera, powered by a high performance SoC (dual ARM plus FPGA), packaged in a lightweight aluminum extrusion body, providing different options for various environments – indoors, outdoors, on board of the UAV or even in the open space with no air (cooling is important when you run most of the FPGA resources at full speed). Tons of potential possibilities, but the finished camera did not seem too exciting – there are so many similar looking devices available.

NC393 camera, front view

NC393 camera, back panel view. Includes DC power input (12-36V and 20-75V options), GigE, microSD card (bootable), microUSB(type B) connector for a system console with reset and boot source selection, USB/eSATA combo connector, microUSB(type A) and 2.5mm 4-contact barrel connector for external synchronization I/O

NC393 assembled boards: 10393(system board), 10385 (power supply board), 10389(interface board), 10338e (sensor board) and 103891 - synchronization adapter board, view from 10389. m.2 2242 SSD shown, bracket for the 2260 format provided. 10389 internal connectors include inter-camera synchronization and two of 3.3VDC+5.0VDC+I2C+USB ones.

NC393 assembled boards: 10393(system board), 10385 (power supply board), 10389(interface board), 10338e (sensor board) and 103891 - synchronization adapter board, view from 10385

10393 system board attached to the heat frame, view from the heat frame. There is a large aluminum heat spreader attached to the other side of the frame with thermal conductive epoxy that provides heat transfer from the CPU without the use of any spring load. Other heat dissipating components use heat pads.

10393 system board attached to the heat frame, view from the 10393 board

10393 system board, view from the processor side

An obvious reason for our dissatisfaction is that the single-sensor camera uses just one of four available sensor ports. Of course it is possible to use more of the freed FPGA resources for a single image processing, but it is not what you can use out of the box. Many of our users buy camera components and arrange them in their custom setup themselves – that does not have a single-sensor limitation and it matches our goals – make it easy to develop a custom system, or sculpture the camera to meet your ideas as stated on our web site. We would like to open the cameras to those who do not have capabilities of advanced mechanical design and manufacturing or just want to try new camera ideas immediately after receiving the product. (more…)

April 21, 2016

Tutorial 01: Access to Elphel camera documentation from 3D model

by olga

We have created a short video tutorial to help our users navigate through 3D models of Elphel cameras. Cameras can be virtually taken apart and put back together which helps to understand the camera configuration and access information about every camera component. Please feel free to comment on the video quality and usefulness, as we are launching a series of tutorials about cameras, software modifications, FPGA development on 10393 camera board, etc. and we would like to receive feedback on them.
(more…)

December 21, 2015

X3D assemblies from any CAD

by Andrey Filippov

Converting mechanical assemblies to X3D models from STEP (ISO 10303) files

Like all manufacturing companies we use mechanical CAD program to design our products. We would love to use Free Software programs for that, but so far even FreeCAD has a warning on their download page “FreeCAD is under heavy development and might not be ready for production use”. We have to use proprietary tools, our choice was the program that natively runs on GNU/Linux we use on our computers. This program generates STEP files that we can send to virtually any machine shop (locally or overseas) and expect to receive the manufactured parts that match our design. For the last 6 years we kept the CAD models for all the camera parts on Elphel Wiki hoping they might be needed not only by the machine shops we order parts from, but also by our users to incorporate (or modify) our products in their systems. (more…)