CS348b2007

Assignment 2: Lazy K-D Tree

Due: Thursday April 26th, 11:59PM

topview mediumrange closeup

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.

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

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

Given what you have learned from studying the existing pbrt k-d tree implementation, implement your own k-d tree acclerator that uses lazy evaluation to gain efficiency for complex scenes. Create the file accelerator/lz-kdtree.cpp in your source tree, and implement your k-d tree in this file. You do not have to write everything from scratch (and we encourage you not to). Feel free to reuse any data structures and functions from kdtree.cpp. You should not need to change any other source files in pbrt, although you are free to do so. Download this zip file, which contains several scene files (You will need to obtain the models for plants-view1.pbrt and plants-view2.pbrt from the scenes directory on the PBRT CD) or /afs/ir/class/cs348b/assignments/assignment2 on the lab machines). Test your implementation using these scenes and compare the results with pbrt's kd-tree implementation. Note that the rendering time recorded by pbrt does not include build time of the acceleration structure. Use ProgressReporter class to record the tree construction time. The evaluation of the quality of your implementation is an important part of this assignment. The number of tree nodes constructed, tree build time (upfront), total rendering time, and number of ray-triangle intersections are all important statistics that you can use to guide your implementation choices.

Suggestions

Step 4: Evaluation

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