This week I did research comparing different 3d reconstruction options as well as a bit of research on texturing 3d scans. There are several possible scanning options, some of which were suggested by Professor Tamal. These include RGB-D camera (gives depth information on each pixel using coded light), time-of-flight single point (one laser point with depth using time-of-flight), time-of-flight vertical line (like a barcode), and time-of-flight laser 2D depth map.
The time-of-flight single point laser scanner was the lowest priced option, but it was difficult finding many papers that used this method due to being very prone to mechanical errors, as well as being rather time-costly and complex mechanically. There were a few possible ways of executing this method which included the spiral method, where the laser point would slowly move down vertically while the object rotated. Depending on the controlling mechanism, this method would be prone to missing a lot of points scanned, especially if the laser shudders or other mechanical errors. A way to make this more efficient would simply be to use several different laser points since each was not very costly; however, the same issues would still arise.
The RGB-D camera using coded light was one idea we were very interested in, especially given that the camera would already help us do some of the processing to get the depth data. This would also allow for texture mapping, something that would be missing from the time-of-flight sensors (unless we combine those with camera data). This method would be less prone to error depending on our depth camera, and among the few options we looked at, we would most likely use one that can give within 1mm accuracy for depth data. This method would also allow for correction for bias using color data potentially. The price is also not too expensive (less than $100 for the coded light depth camera), which fits our requirements. However, we may need to do some work in figuring out confidence intervals for the accuracy ranges and determining if this reconstruction method would be able to figure out the depth accurate enough to fit our requirements for accuracy.
The laser-based approaches are still intriguing since time-of-flight lasers can usually give micrometer accuracy since we determine the exact distance using wavelength and time-of-flight data. This led to an idea from Professor Tamal to use LIDAR (1D/2D) for the scanning. The 1D LIDAR would behave like a barcode scanner with a vertical line to scan and the object rotating, but there may be certain complexities to explore with this method, and there have not been a lot of previous work using this method. The 2D LIDAR would be even more accurate and gives an accurate depth map, but it would cost quite a bit more. This method is certainly very promising and deserves extensive research to compare with the RGB-D camera method.
All of these methods would potentially require some filtering or smoothing techniques to remove the noise from the data, but the RGB-D camera and the 2D LIDAR would probably give us the easiest time in managing and converting the data into a 3D point cloud. Since the data is 2D, however, we would need to cross reference points and map several scans from different angles back into the same object, which would be one of the main algorithmic complexities of our project. We would also be able to leverage the rotational position of the platform to help us determine which pixel maps to which exact 3D point in space.
Thus, in the coming week, I will have to dive deep into researching the RGB-D and 2D LIDAR methods and doing more extensive comparisons between the two, and referencing their qualities back to our requirements. So far, a lot of our research has been very breadth based, since we were considering a large variety of options, such as previously considering using two cameras and computer vision to do the scanning. However, my research goal this week is to narrow down on the specific idea we use and justify it with qualities we look for based on our requirements. I will also be doing more research on piecing together scans from different angles, as well as working out math to figure out a 3D point given a pixel, depth, and camera position, as this will be necessary information for our algorithm later on regardless which scanning mechanism we choose (both will output depth data).
Table for Comparing Possible Sensors
| Sensor | Cost | Mechanical Complexity | Pre-Filtering Complexity (estimated) | Potential Sources of Error | Texture Mapping | Algorithmic Implications |
| RGB-D Camera (structured/coded light) | ~$70 | Low | High | Less accurate than laser time-of-flight approaches, noise | Possible | Color may allow better matching with ICP |
| Time-of-Flight single point | ~$10 per sensor | High | Medium | High risk of mechanical errors | No | Direct computation of point cloud, no ICP |
| Time-of-Flight vertical line | ~$130? | Medium | Low | Noise (but less error than 2D?) | No | Direct computation of point cloud, no ICP |
| Time-of-Flight 2D depth map | ~$200? | Low | Medium | Noise | No | Direct ICP available |