SDF Collision Framework: Core Concepts Explained
Task 009: SDF-Based Collision Framework
Hey guys, let's dive into Task 009, the SDF-Based Collision Framework. This is a super important piece of the puzzle for our cloth simulation project. It's all about making sure our cloth interacts realistically with the world around it, like colliding with objects. This framework will give us the foundation for all the cool collision stuff we want to do. Think of it as the backbone for how our cloth will interact with the environment. We're talking about spheres, planes, and the way they bounce and react. It's going to be awesome!
Overview
So, what's the plan? We're building a robust system using Signed Distance Fields (SDFs). If you're new to the concept, don't worry; it's pretty straightforward. Basically, an SDF tells us how far a point is from the surface of an object and whether that point is inside or outside the object. This is a key concept that we are using for the collision detection pipeline. Our system needs to handle collisions between cloth particles and both spheres and planes – those are our basic shapes, for now. We will support multiple colliders simultaneously with spatial organization which is a fancy way of saying we're going to optimize how we check for collisions so it doesn't slow everything down. Everything is done on the GPU to keep things fast! This is not a simple task, but it's critical to get right. It will be used in the whole cloth simulation.
Technical Requirements
Alright, let's get into the nitty-gritty. We need a Signed Distance Field (SDF) based collision detection pipeline. That means that the system will use SDF to detect collisions. We're kicking things off with support for sphere and plane collision primitives. These are the fundamental shapes. The structure will support multiple colliders at the same time and is done on the GPU for efficiency. We need to be efficient with our GPU-based distance queries and collision detection to get the best result with all of this. Here's how the structure would look like for SphereCollider and PlaneCollider:
#[repr(C)]
#[derive(Copy, Clone, bytemuck::Pod, bytemuck::Zeroable)]
struct SphereCollider {
center: Vec3,
radius: f32,
material_id: u32,
is_active: u32,
_padding: [u32; 2],
}
#[repr(C)]
#[derive(Copy, Clone, bytemuck::Pod, bytemuck::Zeroable)]
struct PlaneCollider {
normal: Vec3,
distance: f32,
material_id: u32,
is_active: u32,
_padding: [u32; 2],
}
Here's how the GPU collision detection pipeline will work. This is how we handle collisions in the wgsl:
@compute @workgroup_size(64, 1, 1)
fn detect_collisions(
@builtin(global_invocation_id) id: vec3<u32>
) {
let particle_id = id.x;
if (particle_id >= particle_count) { return; }
let position = positions[particle_id].xyz;
var min_distance = 1e10;
var collision_normal = vec3<f32>(0.0, 1.0, 0.0);
var has_collision = false;
// Test against all sphere colliders
for (var i = 0u; i < sphere_count; i++) {
let sphere = sphere_colliders[i];
if (sphere.is_active == 0u) { continue; }
let distance = sdf_sphere(position, sphere.center, sphere.radius);
if (distance < min_distance) {
min_distance = distance;
collision_normal = normalize(position - sphere.center);
has_collision = distance < 0.0;
}
}
// Test against all plane colliders
for (var i = 0u; i < plane_count; i++) {
let plane = plane_colliders[i];
if (plane.is_active == 0u) { continue; }
let distance = sdf_plane(position, plane.normal, plane.distance);
if (distance < min_distance) {
min_distance = distance;
collision_normal = plane.normal;
has_collision = distance < 0.0;
}
}
// Store collision results
collision_data[particle_id] = CollisionResult(
min_distance,
collision_normal,
select(0u, 1u, has_collision)
);
}
Implementation Details
Let's look at the core building blocks, starting with the SDF function library. This is where we define how to calculate the distance from a point to a sphere, plane, or box (in the future). This is how the functions look like. Note that the box is not in use at the moment. We will work with spheres and planes.
// Sphere SDF
fn sdf_sphere(point: vec3<f32>, center: vec3<f32>, radius: f32) -> f32 {
return distance(point, center) - radius;
}
// Plane SDF
fn sdf_plane(point: vec3<f32>, normal: vec3<f32>, distance: f32) -> f32 {
return dot(point, normal) - distance;
}
// Box SDF (for future extension)
fn sdf_box(point: vec3<f32>, center: vec3<f32>, size: vec3<f32>) -> f32 {
let d = abs(point - center) - size * 0.5;
return length(max(d, vec3<f32>(0.0))) + min(max(d.x, max(d.y, d.z)), 0.0);
}
Next up: Collision Data Management. We will manage the data for our colliders and the results of collision tests. This involves structures to store collider information, along with functions to add colliders and upload data to the GPU. This is a rust implementation.
struct CollisionSystem {
sphere_colliders: Vec<SphereCollider>,
plane_colliders: Vec<PlaneCollider>,
collision_buffer: wgpu::Buffer,
collision_results: wgpu::Buffer,
max_colliders: u32,
}
impl CollisionSystem {
pub fn add_sphere(&mut self, center: Vec3, radius: f32) -> ColliderHandle {
let collider = SphereCollider {
center,
radius,
material_id: 0,
is_active: 1,
_padding: [0; 2],
};
self.sphere_colliders.push(collider);
ColliderHandle::Sphere(self.sphere_colliders.len() - 1)
}
pub fn add_plane(&mut self, normal: Vec3, distance: f32) -> ColliderHandle {
let collider = PlaneCollider {
normal,
distance,
material_id: 0,
is_active: 1,
_padding: [0; 2],
};
self.plane_colliders.push(collider);
ColliderHandle::Plane(self.plane_colliders.len() - 1)
}
pub fn update_buffers(&mut self, queue: &wgpu::Queue) {
// Upload collider data to GPU
queue.write_buffer(&self.collision_buffer, 0, bytemuck::cast_slice(&self.sphere_colliders));
// ... plane colliders
}
}
To keep things running smoothly, we also need Spatial Organization. This is all about making collision detection efficient, especially when there are a lot of colliders. We're going to use a spatial grid to organize our colliders. This lets us quickly narrow down which colliders a particle might be colliding with, avoiding unnecessary calculations. This makes the process much faster!
struct CollisionSpatialGrid {
grid_size: Vec3,
cell_size: f32,
grid_cells: Vec<Vec<ColliderHandle>>,
}
impl CollisionSpatialGrid {
fn get_potential_colliders(&self, position: Vec3) -> &[ColliderHandle] {
let cell_index = self.position_to_cell(position);
&self.grid_cells[cell_index]
}
fn update_collider_placement(&mut self, handle: ColliderHandle, bounds: AABB) {
// Update spatial grid with collider bounds
}
}
And, finally, we have Collision Result Storage. This is where we store the results of the collision tests – the distance to the closest surface, the surface normal, and whether a collision actually happened. It's how the rest of the system knows what's going on.
#[repr(C)]
#[derive(Copy, Clone, bytemuck::Pod, bytemuck::Zeroable)]
struct CollisionResult {
distance: f32, // Signed distance to closest surface
normal: Vec3, // Surface normal at collision point
has_collision: u32, // Boolean flag for collision occurrence
material_id: u32, // Material properties for response
}
Acceptance Criteria
So, how do we know we've done a good job? Well, we have a list of things to check:
- Sphere Collision: Ensure accurate collision detection with spherical obstacles.
- Plane Collision: Check correct collision detection with infinite planes.
- Multiple Colliders: Verify support for simultaneous collisions with multiple objects.
- Performance: The collision detection must complete in under 3ms per frame on a GTX 1660 graphics card.
- Accuracy: Make sure there are no false positives or negatives in collision detection.
- Scalability: Confirm support for up to 32 colliders without significant performance loss.
Dependencies
To get this done, we need some pre-existing pieces and systems that will help us:
Prerequisites
- Task 001 (GPU Buffer Management System): to have the collision data buffers.
- Task 002 (Basic XPBD Physics Pipeline): for the collision constraints.
- Mathematical foundation for SDF functions.
Blocks
- Task 010 (Collision Response System): will use collision detection data.
- Task 011 (Debug Visualization): to see the results.
Can Work In Parallel With
- Task 006 (Advanced Force System): an independent physics feature.
- Task 007 (GPU-based Normal Computation): a different subsystem.
Testing Strategy
Testing is super important, guys! We're going to do several tests to make sure everything works as expected.
SDF Function Tests
First, we will test to ensure the function SDF functions are correct.
#[test]
fn sdf_sphere_accuracy() {
// Test sphere SDF against analytical solutions
assert!((sdf_sphere(Vec3::ZERO, Vec3::ZERO, 1.0) - (-1.0)).abs() < 1e-6);
assert!((sdf_sphere(Vec3::new(2.0, 0.0, 0.0), Vec3::ZERO, 1.0) - 1.0).abs() < 1e-6);
}
#[test]
fn sdf_plane_accuracy() {
// Test plane SDF against analytical solutions
let normal = Vec3::new(0.0, 1.0, 0.0);
assert!((sdf_plane(Vec3::new(0.0, 1.0, 0.0), normal, 0.0) - 1.0).abs() < 1e-6);
}
Integration Tests
Then, we will run integration tests. Integration tests are where we bring all the pieces together to see if they play well.
- Multi-collider collision scenarios.
- Edge case testing (tangent surfaces, multiple simultaneous collisions).
- Performance scaling with collider count.
- Memory usage validation.
Technical Specifications
Here's a quick look at the technical side. The Buffer Layout:
struct CollisionBuffers {
sphere_colliders: Buffer<SphereCollider>, // Sphere collision primitives
plane_colliders: Buffer<PlaneCollider>, // Plane collision primitives
collision_results: Buffer<CollisionResult>, // Per-particle collision results
collider_counts: Buffer<u32>, // [sphere_count, plane_count]
}
And the Shader Interface:
@group(1) @binding(0) var<storage, read> sphere_colliders: array<SphereCollider>;
@group(1) @binding(1) var<storage, read> plane_colliders: array<PlaneCollider>;
@group(1) @binding(2) var<storage, read_write> collision_data: array<CollisionResult>;
@group(1) @binding(3) var<uniform> collider_info: ColliderInfo;
struct ColliderInfo {
sphere_count: u32,
plane_count: u32,
collision_threshold: f32,
_padding: u32,
}
Performance Targets
We have some performance targets to hit, too!
- Collision Detection: Keep it under 3ms per frame for 1024 particles and 16 colliders.
- Memory Usage: Keep collision data structures under 1MB.
- Scalability: We want the performance to scale linearly as we add more colliders.
- GPU Utilization: We want our GPU to be working at over 80% capacity during collision detection.
Risk Factors
Let's talk about what could go wrong. Potential risks include:
- Performance Scaling: Too many colliders might slow things down.
- Memory Bandwidth: Multiple SDF evaluations could be too much for memory.
- Precision Issues: Floating-point precision might cause problems near surfaces.
- Spatial Coherence: Poor spatial organization could cause cache misses.
Implementation Notes
Here are some things to consider during implementation:
SDF Algorithm Selection
- Choose simple, fast SDF functions over complex but accurate ones.
- Consider approximations for complex shapes to maintain performance.
- Implement distance field gradients for smooth normal computation.
Spatial Optimization
- Consider implementing broad-phase collision culling.
- Use spatial grids or other acceleration structures for many colliders.
- Profile memory access patterns for optimization opportunities.
Future Extensions
- Design the system to easily add new primitive types (capsules, boxes).
- Consider support for animated/moving colliders.
- Plan for mesh-based collision using distance field textures.
Debug and Validation
- Implement collision visualization for debugging.
- Add collision count statistics for performance monitoring.
- Consider collision history tracking for temporal coherence.
This should give you a solid understanding of our SDF-Based Collision Framework. We will use all these functions to achieve the expected result.