The Lab university I work at is in the process of purchasing a laser scanner for scanning 3D objects. All along from the start we've been trying to find a scanner that is able to capture real RAW normals from the actual scanned surface. It seems that most scanners only capture points and then the software interpolates to find the normal of the approximate surface.
Does anybody know if there is actually such a thing as capturing raw normals? Is there a scanner that can do this and not interpolate the normals from the point data?
Highly unlikely. Laser scanning is done using ranges. What you want would be combining two entirely different techniques. Normals could be evaluated with higher precision using well controlled lighting etc, but requiring a very different kind of setup. Also consider the sampling problem: What good is a normal with higher resolution than your position data?
If you already know the bidirectional reflectance distribution function of the material that composes your 3D object, it is possible that you could use a gonioreflectometer to compare the measured BRDF at a point. You could then individually optimize a computed normal at that point by comparing a hypothetical BRDF against the actual measured value.
Admittedly, this would be a reasonably computationally-intensive task. However, if you are only going through this process fairly rarely, it might be feasible.
For further information, I would recommend that you speak with either Greg Ward (Larson) of Radiance fame or Peter Shirley at NVIDIA.
Here is an example article of using structured light to reconstruct normals from gradients.
Shape from 2D Edge Gradients
I didn't find the exact article I was looking for, but this seems to be on the same principle.
You can reconstruct normals from the angle and width of the stripe after being deformed on the object.
You could with a structured light + camera setup.
The normal would come from the angle betwen the projected line and the position on the image. As the other posters point out - you can't do it from a point laser scanner.
Capturing raw normals is almost always done using photometric stereo. This almost always requires placing some assumptions on the underlying reflectance, but even with somewhat inaccurate normals you can often do well when combining them with another source of data:
Really nice code for combining point clouds (from a laser scan for example) with surface normals: http://www.cs.princeton.edu/gfx/pubs/Nehab_2005_ECP/
Related
Disclaimer: I'm not 100% on whether this is a well-formed question, so please feel free to comment and suggest improvements. I'll be actively looking out for ways to improve this question.
I have a triangle mesh, let's say the Stanford Bunny. Now, I want to raycast a ray from a source point in 3D along a 3D direction vector, and identify just the first intersection of that ray with the triangle mesh.
I already have a naive implementation cooked up. However, I'm looking for a more advanced implementation. In particular, I'll be casting many millions of rays in many directions, so I'm looking for a multi-threaded or GPU-accelerated implementation.
I have to believe that there must be some pretty complete projects online, as raycasting triangle meshes is a fundamental part of 3D computer graphics. However, I can't find anything beyond personal projects, which leads me to believe that I am using the wrong search terms, or something pretty simple along those lines.
I am looking for suggestions on existing tools that can raytrace polygonal meshes.
If all you need to do is find the distance to the mesh for millions of rays. Then it might be a good idea to look up CUDA raytracing tutorial online. This will show you how to cast many millions of rays. In most tutorials, raytracing is used to render to the screen with the camera matrix. However, this is not necessary. Simply adjust the rays starting parameters to what you need them to be such as 3D vector and position. Then output the data back to the CPU. Be weary of the bandwidth between the GPU and CPU sending millions of intersection points between the CPU and GPU can make the program run exceptionally slow.
I am sketching out a new simulation that will involve thousands of ships moving around on Earth's oceans and interacting over long periods of time. So, lots of "intersection detection" for sensor and communications ranges, as well as region detection for various environmental conditions. We'll assume a spherical earth, not WGS84. This is an event-step simulation that spits out metrics, not a real time game or anything like that.
A question is to use Cartesian coordinates (Earth-Centered, Earth-Fixed) or Geodic/polar coordinates. With polar coordinates a ship's track would be internally represented as a series of lat/lon waypoints with times and a great circle paths between them. With a Cartesian representation the waypoints would be connected with polyline renderings of the great circle between them.
The reason this is a question is I suspect that by sticking to a Cartesian data model it becomes possible to use various geometry libraries that are performance tuned, and even offer up SIMD/GPU performance advantages. The polar coordinates would probably be the more natural way to proceed if writing everything from scratch. But I suspect that by keeping things Cartesian I will have greater access to better and faster libraries. Is this an invalid line of thought? Another consideration is that I know polar coordinate calculations tend to get really screwy when near the poles.
Just curious if somebody with experience could save me a whole lot of time prototyping some scenarios both ways.
It often works well to represent directions as unit vectors instead of angles. Rotation of a vector by another angle becomes a 2x2 or 3x3 matmul (efficient with SIMD, but still more expensive than an FP add of two numbers in radians), but you very rarely need sin/cos.
You may occasionally want atan2 to get an angle, but usually not inside tight loops.
Intersection-detection can be very efficient (with SIMD) for XYZ coordinates given another XYZ + range. I'm not sure how efficiently you could check which lat/lon pairs were within range of a given point, not a problem I've looked at.
IDK what kind of stuff you'd find in existing libraries, or what you'd want to do with it.
I am looking for an algorithm that given two meshes could clip one using another.
The simplest form of this is clipping a mesh using a plane. I've already implemented that by following something similar to what is described here.
What it does is basically inspecting all mesh vertices and triangles with respect to the plane (the plane's normal and point are given). If the triangle is completely above the plane, it is left untouched. If it falls completely below the plane, it is discarded. If some of the edges of the triangle intersect with the plane, the intersecting points with the plane are calculated and added as the new vertices. Finally a cap is generated for the hole on the place the mesh was cut.
The problem is that the algorithm assumes that the plane is unlimited, therefore whatever is in its path is clipped. In the simplest form, I need an extension of this without the assumption of a plane of "infinite" size.
To clarify, imagine that we have a 3D model of a desk with 2 boxes on it. The boxes are adjacent (but not touching or stacked). The user will define a cutting plane of a limited width and height underneath the first box and performs the cut. We end up with a desk model (mesh) with a box on it and another box (mesh) that can be freely moved around/manipulated.
In the general form, I'd like the user to be able to define a bounding box for the box he/she wants to separate from the desk model and perform the cut using that bounding box.
If I could extend the algorithm I already have to an algorithm with limited-sized planes, that would be great for now.
What you're looking for are constructive solid geometry/boolean algorithms with arbitrary meshes. It's considerably more complex than slicing meshes by an infinite plane.
Among the earliest and simplest research in this area, and a good starting point, is Constructive Solid Geometry for Polyhedral Objects by Trumbore and Hughes.
http://cs.brown.edu/~jfh/papers/Laidlaw-CSG-1986/main.htm
From the original paper:
More elaborate solutions extend upon this subject with a variety of data structures.
The real complexity of the operation lies in the slicing algorithm to slice one triangle against another. The nightmare of implementing robust CSG lies in numerical precision. It's easy when you involve objects far more complex than a cube to run into cases where a slice is made just barely next to a vertex (at which point you have the tough decision of merging the new split vertex or not prior to carrying out more splits), where polygons are coplanar (or almost), etc.
So I suggest initially erring on the side of using very high-precision floating point numbers, possibly even higher than double precision to focus on getting something working correctly and robustly. You can optimize later (first pass should be to use an accelerator like an octree/kd-tree/bvh), but you'll avoid many headaches this way in your first iteration.
This is vastly simpler to implement at render time if you're focusing on a raytracer rather than a modeling software, e.g. With raytracers, all you have to do to do this kind of arbitrary clipping is pretend that an object used to subtract from another has its polygons flipped in the culling process, e.g. It's easy to solve robustly at the ray level, but quite a bit harder to do robustly at the geometric level.
Another thing you can do to make your life so much easier if you can afford it is to voxelize your object, find subtractions/additions/unions of voxels, and then translate the voxels back into a mesh. This is so much easier to make robust, but harder to do efficiently and the voxel->polygon conversion can get quite involved if you want better results than what marching cubes provide.
It's a really tough area to do extremely well and requires perseverance, and thus the reason for the existence of things like this: http://carve-csg.com/about.
If someone is interested, currently there is a solution for this problem in CGAL library. It allows clipping one triangular mesh using another mesh as bounding volume. The usage example can be found here.
I am using Java to write a very primitive 3D graphics engine based on The Black Art of 3D Game Programming from 1995. I have gotten to the point where I can draw single color polygons to the screen and move the camera around the "scene". I even have a Z buffer that handles translucent objects properly by sorting those pixels by Z, as long as I don't show too many translucent pixels at once. I am at the point where I want to add lighting. I want to keep it simple, and ambient light seems simple enough, directional light should be fairly simple too. But I really want point lighting with the ability to move the light source around and cast very primitive shadows ( mostly I don't want light shining through walls ).
My problem is that I don't know the best way to approach this. I imagine a point light source casting rays at regular angles, and if these rays intersect a polygon it will light that polygon and stop moving forward. However when I think about a scene with multiple light sources and multiple polygons with all those rays I imagine it will get very slow. I also don't know how to handle a case where a polygon is far enough away from a light source that if falls in between two rays. I would give each light source a maximum distance, and if I gave it enough rays, then there should be no point within that distance that any two rays are too far apart to miss a polygon, but that only increases my problem with the number of calculations to perform.
My question to you is: Is there some trick to point light sources to speed them up or just to organize it better? I'm afraid I'll just get a nightmare of nested for loops. I can't use openGL or Direct3D or any other cheats because I want to write my own.
If you want to see my results so far, here is a youtube video. I have already fixed the bad camera rotation. http://www.youtube.com/watch?v=_XYj113Le58&feature=plcp
Lighting for real time 3d applications is (or rather - has in the past generally been) done by very simple approximations - see http://en.wikipedia.org/wiki/Shading. Shadows are expensive - and have generally in rasterizing 3d engines been accomplished via shadow maps & Shadow Volumes. Point lights make shadows even more expensive.
Dynamic real time light sources have only recently become a common feature in games - simply because they place such a heavy burden on the rendering system. And these games leverage dedicated graphics cards. So I think you may struggle to get good performance out of your engine if you decide to include dynamic - shadow casting - point lights.
Today it is commonplace for lighting to be applied in two ways:
Traditionally this has been "forward rendering". In this method, for every vertex (if you are doing the lighting per vertex) or fragment (if you are doing it per-pixel) you would calculate the contribution of each light source.
More recently, "deferred" lighting has become popular, wherein the geometry and extra data like normals & colour info are all rendered to intermediate buffers - which is then used to calculate lighting contributions. This way, the lighting calculations are not dependent on the geometry count. It does however, have a lot of other overhead.
There are a lot of options. Implementing anything much more complex than some the basic models that have been used by dedicated graphics cards over the past couple of years is going to be challenging, however!
My suggestion would be to start out with something simple - basic lighting without shadows. From there you can extend and optimize.
What are you doing the ray-triangle intersection test for? Are you trying to light only triangles which the light would reach? Ray-triangle
intersections for every light with every poly is going to be very expensive I think. For lighting without shadows, typically you would
just iterate through every face (or if you are doing it per vertex, through every vertex) and calculate & add the lighting contribution per light - you would do this just before you start rasterizing as you have to pass through all polys in anycase.
You can calculate the lighting by making use of any illumination model, something very simple like Lambertian reflectance - which shades the surface based upon the dot product of the normal of the surface and the direction vector from the surface to the light. Make sure your vectors are in the same spaces! This is possibly why you are getting the strange results that you are. If your surface normal is in world space, be sure to calculate the world space light vector. There are a bunch of advantages for calulating lighting in certain spaces, you can have a look at that later on, for now I suggest you just get the basics up and running. Also have a look at Blinn-phong - this is the shading model graphics cards used for many years.
For lighting with shadows - look into the links I posted. They were developed because realistic lighting is so expensive to calculate.
By the way, LaMothe had a follow up book called Tricks of the 3D Game Programming Gurus-Advanced 3D Graphics and Rasterization.
This takes you through every step of programming a 3d engine. I am not sure what the black art book covers.
I will be start working on a robotics project which involves a mobile robot that has mounted 2 cameras (1.3 MP) fixed at a distance of 0.5m in between.I also have a few ultrasonic sensors, but they have only a 10 metter range and my enviroment is rather large (as an example, take a large warehouse with many pillars, boxes, walls .etc) .My main task is to identify obstacles and also find a roughly "best" route that the robot must take in order to navigate in a "rough" enviroment (the ground floor is not smooth at all). All the image processing is not made on the robot, but on a computer with NVIDIA GT425 2Gb Ram.
My questions are :
Should I mount the cameras on a rotative suport, so that they take pictures on a wider angle?
It is posible creating a reasonable 3D reconstruction based on only 2 views at such a small distance in between? If so, to what degree I can use this for obstacle avoidance and a best route construction?
If a roughly accurate 3D representation of the enviroment can be made, how can it be used as creating a map of the enviroment? (Consider the following example: the robot must sweep an fairly large area and it would be energy efficient if it would not go through the same place (or course) twice;however when a 3D reconstruction is made from one direction, how can it tell if it has already been there if it comes from the opposite direction )
I have found this response on a similar question , but I am still concerned with the accuracy of 3D reconstruction (for example a couple of boxes situated at 100m considering the small resolution and distance between the cameras).
I am just starting gathering information for this project, so if you haved worked on something similar please give me some guidelines (and some links:D) on how should I approach this specific task.
Thanks in advance,
Tamash
If you want to do obstacle avoidance, it is probably easiest to use the ultrasonic sensors. If the robot is moving at speeds suitable for a human environment then their range of 10m gives you ample time to stop the robot. Keep in mind that no system will guarantee that you don't accidentally hit something.
(2) It is posible creating a reasonable 3D reconstruction based on only 2 views at such a small distance in between? If so, to what degree I can use this for obstacle avoidance and a best route construction?
Yes, this is possible. Have a look at ROS and their vSLAM. http://www.ros.org/wiki/vslam and http://www.ros.org/wiki/slam_gmapping would be two of many possible resources.
however when a 3D reconstruction is made from one direction, how can it tell if it has already been there if it comes from the opposite direction
Well, you are trying to find your position given a measurement and a map. That should be possible, and it wouldn't matter from which direction the map was created. However, there is the loop closure problem. Because you are creating a 3D map at the same time as you are trying to find your way around, you don't know whether you are at a new place or at a place you have seen before.
CONCLUSION
This is a difficult task!
Actually, it's more than one. First you have simple obstacle avoidance (i.e. Don't drive into things.). Then you want to do simultaneous localisation and mapping (SLAM, read Wikipedia on that) and finally you want to do path planning (i.e. sweeping the floor without covering area twice).
I hope that helps?
I'd say no if you mean each eye rotating independently. You won't get the accuracy you need to do the stereo correspondence and make calibration a nightmare. But if you want the whole "head" of the robot to pivot, then that may be doable. But you should have some good encoders on the joints.
If you use ROS, there are some tools which help you turn the two stereo images into a 3d point cloud. http://www.ros.org/wiki/stereo_image_proc. There is a tradeoff between your baseline (the distance between the cameras) and your resolution at different ranges. large baseline = greater resolution at large distances, but it also has a large minimum distance. I don't think i would expect more than a few centimeters of accuracy from a static stereo rig. and this accuracy only gets worse when you compound there robot's location uncertainty.
2.5. for mapping and obstacle avoidance the first thing i would try to do is segment out the ground plane. the ground plane goes to mapping, and everything above is an obstacle. check out PCL for some point cloud operating functions: http://pointclouds.org/
if you can't simply put a planar laser on the robot like a SICK or Hokuyo, then i might try to convert the 3d point cloud into a pseudo-laser-scan then use some off the shelf SLAM instead of trying to do visual slam. i think you'll have better results.
Other thoughts:
now that the Microsoft Kinect has been released, it is usually easier (and cheaper) to simply use that to get a 3d point cloud instead of doing actual stereo.
This project sounds a lot like the DARPA LAGR program. (learning applied to ground robots). That program is over, but you may be able to track down papers published from it.