CS348b2007

Assignment 2: Lazy K-D Tree

Due: Thursday April 26th, 11:59PM

Please link to your final Assignment 2 writeup wiki page from Assignment2Writeups.

Description

In this assignment, you will improve pbrt's implementation of the k-d tree accelerator. As described in Chapter 4 of the textbook, before rendering, pbrt constructs a k-d tree containing all geometric primitives in the scene. This approach can be inefficient for several reasons. First, as you will observe in this assignment, building a complete k-d tree for a scene containing many geometric elements can be very costly. Depending on the camera's location and how objects in the scene occlude others from view, it is possible that many k-d tree nodes created in the initial build process may never be hit by any rays. In this case, both computation spent building these nodes and, more importantly, the memory used to store them is wasted. Your objective in this assignment is to modify pbrt's k-d tree accelerator to lazily build the acceleration structure as rays are shot through the scene.

topview mediumrange closeup

In the images above show the same scene rendered from three different camera positions. The scene contains 76 killeroos and each killeroo is a subdivision surface shape with a base mesh consisting of 8316 triangles. Six of these killeroos (the baby killeroos) undergo one level of subdivision, so the resulting meshes contain 33262 triangles. In total, the killeroos constitute 775,692 triangles. Regardless of the camera position used, pbrt preprocesses the scene into a k-d tree containing 9.7 million nodes. pbrt's statistics for the scene are given below:

Geometry
    Total shapes created                                    781.9k
    Triangle Ray Intersections                              656.7k:4838.6k (13.57%)
    Triangles created                                       781.7k
Kd-Tree Accelerator
    Avg. number of primitives in leaf nodes                 12.148M:4.886M (2.49x)
    Interior kd-tree nodes made                             4.886M
    Leaf kd-tree nodes made                                 4.886M
    Maximum number of primitives in leaf node               395

This preprocess can expensive. On my laptop the k-d tree build takes about 30 seconds, while rendering the scene using the k-d tree requires approximately 25 seconds. Additionally, it consumes a large amount of memory. Even with pbrt's compact 8 byte representation of a tree node, the 9.7 million nodes consume 77MB of memory (plus an additional 48MB to store the references to primitives from tree leaf nodes). One solution is to build the tree on demand while tracing rays through the scene. Delayed computation or lazy evaluation is a common technique used in many computer science algorithms. The point of this assignment is to implement a lazy kd-tree to improve pbrt's performance when rendering complex scenes whose geometry extends well beyond the space traversed by the rays generated to render an image, such as in images at center and at right. Your implementation must be lazy in two key ways: (1) kd-tree nodes will be constructed dynamically as needed, and (2) scene objects will be refined into their primitive components only when required.

Step 1: Understanding pbrt's k-d tree

It is critical that you first obtain a detailed understanding of the current k-d tree implementation in pbrt. Read Section 4.4 of the textbook and make sure you can follow the code in kdtree.cpp, located in the accelerator directory of the code base. You should be able to answer the following questions about the current implementation (we do not expect you to hand in answers to these questions).

Step 2: Time pbrt's original k-d tree build

The rendering time reported by pbrt does not include the time spent building the acceleration structure. Use the ProgressReporter class to measure the amount of time it takes pbrt to create a k-d tree (see the CreateAccelerator function in kdtree.cpp. You'll need to wrap the creation of the KDtreeAccel like this:

  ProgressReporter progress(1, "Building KDTree");

  KdTreeAccel* accel = new KdTreeAccel(prims, isectCost, travCost,
                                       emptyBonus, maxPrims, maxDepth);

  progress.Update();
  progress.Done();

Step 3: Implement a Lazy Kd-tree

Begin by downloading the Assignment 2 pbrt scene files located at http://graphics.stanford.edu/courses/cs348b-07/assignment2/assignment2.zip.

manykilleroos.pbrt is the scene file you will render in this assignment. This main scene file includes the files killeroo_subdiv0.pbrt, killeroo_subdiv1.pbrt, and killeroo_row.pbrt. At the top of manykilleroos.pbrt you will notice 3 different camera positions specified by different LookAt transforms which correspond to the images shown above.

Given what you have learned from studying the existing pbrt k-d tree implementation, modify kdtree.cpp so that the k-d tree accelerator that uses lazy evaluation to gain efficiency for complex scenes. Be sure to keep a copy of the original k-d tree accelerator implementation around for use in later assignments and for debugging ans test in this assignment. (Alternatively you may want to implement your lazy k-d tree accelerator as an entirely different class in pbrt. This is more work, as it requires modification of the pbrt project and Makefiles, and some surrounding pbrt code, but would allow you to select between your and the original k-d tree implementation by only changing the scene file, not recompiling the kdtreee accelerator module. Ask for help if you cannot figure out how to do this on your own, and wish to do so).

This assignment is extremely open ended, and there is more than one way to correctly implement the assignment. Your grade on this assignment will depend largely on your ability to describe the strategies you tried and your evaluation of them on the test scene. Here are some suggestions to get you started:

Advanced Suggestions

Step 4: Evaluation Part 1

The evaluation of the quality of your implementation is an important part of this assignment. Test your lazy k-d tree implementation by rendering all 3 views of the killeroo scene and compare your results with the regular pbrt k-d tree implementation. Write up a report that addresses the following:

pbrt KD Tree

my Lazy KD Tree

Ratio

build time (secs)

-

-

-

total time (secs)

-

-

-

nodes made

-

-

-

Triangle ray intersections

-

-

-

Step: 5 Render on a BIG scene

Lastly, we'd like you increase subdivision on all 76 killeroos in the scene to be at least level 1. To do this, modify killeroo_row.pbrt so that the file includes copies of killeroo_subdiv1.pbrt instead of killeroo_subdiv0.pbrt. Now the scene, fully refined, contains 2.5 million triangles. Render the scene from the close up viewpoint using your lazy k-d tree implementation, and compare statistics with the original pbrt implementation 'if possible. Note that on systems with only about a gig of RAM a non-lazy k-d tree implementation will cause pbrt to swap. We hope your system can complete a rendering using pbrt's original k-d tree implementation. Note that if you lazily built a tree, but did not implement lazy refinement, your memory footprint will continue to be very large (why is this the case?). This test highlights the importance of lazy refinement and building. It makes rendering very large scenes possible when the entire scene is not in view. Include the results of this test in your writeup.

Step 6: Submission

#acl YourWikiUsername:read,write,admin All:

You can link to the images to display them on your wikipage using attachment:filename.

Grading

This assignment (as well as all future assignments in the class) will be graded on a 4 point scale:

Extra credit will be given for exceptional experimentation with heuristics or techniques to improve the performance or quality (for example: the number of nodes created) of your lazy k-d tree implementation.

last edited 2007-05-06 23:02:39 by KayvonFatahalian