Crafting a Custom Physics Engine: Solving Real-World Collision Challenges

This overview reflects widely shared professional practices as of May 2026; verify critical details against current official guidance where applicable.

Building a custom physics engine from scratch is a rite of passage for many game developers and simulation engineers. While off-the-shelf engines like PhysX or Bullet are powerful, they can be overkill or opaque when you need fine-grained control over performance or behavior. Crafting your own engine teaches you the fundamentals of collision detection, impulse resolution, and stability—knowledge that pays dividends even when using existing libraries. However, the path is fraught with subtle bugs: tunneling, jitter, ghost collisions, and stack instability. This guide walks through the core challenges and practical solutions for building a robust collision system, drawing from composite scenarios and common patterns seen in the industry.

The Real-World Stakes: Why Collision Challenges Matter

Collision handling is the heart of any physics engine. A poorly implemented system leads to objects passing through each other, erratic bouncing, or simulation instability that ruins user experience. In a typical project—say, a platformer game or a rigid-body sandbox—the first time you drop a stack of boxes, you'll likely see them explode or sink into the floor. These failures stem from fundamental design decisions: how you detect collisions, how you resolve them, and how you handle edge cases like high-speed motion or resting contacts.

The Tunneling Problem

One of the most common issues is tunneling, where a fast-moving object passes completely through a thin wall in a single time step. This occurs because discrete collision detection only checks for overlaps at discrete intervals. If the object's displacement in one frame is larger than the wall's thickness, the collision is missed. The classic solution is Continuous Collision Detection (CCD), which sweeps the object's shape along its trajectory to find the time of impact. However, CCD is computationally expensive and can introduce its own artifacts, such as false positives with concave shapes. Many teams adopt a hybrid approach: use CCD only for fast-moving objects (e.g., bullets) and discrete detection for the rest.

Resting Contacts and Stacking

Another major challenge is handling resting contacts—when objects sit on top of each other, like a stack of crates. Naive impulse-based resolution can cause jitter or slow convergence. A common technique is to use an iterative solver that processes contacts multiple times per frame, gradually reducing velocity errors. However, tuning the number of iterations and the solver's relaxation factor is tricky: too few iterations leads to sinking, too many kills performance. Many engines also employ a concept of 'slop' or 'baumgarte stabilization' to correct positional errors, but this must be balanced to avoid adding energy to the system.

Performance Constraints

Real-time applications demand that collision detection runs in milliseconds. A brute-force O(n^2) check between all pairs of objects is impractical beyond a few dozen objects. Spatial partitioning structures like grids, octrees, or BVHs (Bounding Volume Hierarchies) are essential. The choice depends on the scene's characteristics: uniform grids work well for evenly distributed objects, while BVHs adapt better to sparse scenes with varying sizes. In one composite scenario, a team building a demolition simulation found that a dynamic BVH with lazy updates gave them a 10x speedup over a naive grid when dealing with thousands of fragments.

Core Frameworks: Broad and Narrow Phase Detection

Collision detection is typically split into two phases: the broad phase, which quickly eliminates pairs that cannot possibly collide, and the narrow phase, which performs precise intersection tests on the remaining candidates. Understanding both phases is critical for building an efficient engine.

Broad Phase Strategies

The broad phase aims to reduce the number of candidate pairs. Common approaches include:

• Sweep and Prune (SAP): Sort axis-aligned bounding boxes (AABBs) along one or more axes and test overlapping intervals. It's simple and fast for scenes with low object density, but performance degrades when objects cluster tightly.
• Spatial Hashing: Divide the world into a grid and hash each cell. Objects in the same or adjacent cells are tested. It's great for uniform distributions but can waste memory on large empty spaces.
• BVH (Bounding Volume Hierarchy): Build a tree of bounding volumes around objects or clusters. Traverse the tree to find overlapping leaves. BVHs are robust for dynamic scenes but require rebuilding or refitting each frame.

Each method has trade-offs. For a physics engine handling a few hundred objects, SAP is often sufficient. For thousands of objects in a large open world, a BVH or hybrid grid-BVH may be better. Many engines implement multiple broad-phase strategies and switch based on scene complexity.

Narrow Phase: The Separating Axis Theorem

The narrow phase performs exact intersection tests. For convex shapes, the Separating Axis Theorem (SAT) is the gold standard. SAT states that if two convex shapes are disjoint, there exists an axis along which their projections do not overlap. By testing a set of potential separating axes (the face normals of each shape and the cross products of edge directions), you can determine if they intersect and compute the penetration depth. SAT works well for boxes, spheres, capsules, and even convex polyhedra. However, it becomes expensive for shapes with many faces. For concave shapes, you must decompose them into convex parts (e.g., using a convex decomposition library like V-HACD) or use alternative methods like GJK (Gilbert-Johnson-Keerthi) combined with EPA (Expanding Polytope Algorithm). GJK is an iterative algorithm that finds the distance between two convex shapes; EPA then computes the penetration depth and contact normal. GJK is often faster than SAT for complex shapes but harder to implement robustly.

Contact Generation

Once a collision is detected, you need to generate contact points—the points where the objects touch. For polygonal shapes, this typically involves clipping the faces of one shape against the other's edges, producing a contact manifold. A good contact manifold includes one or more points, the normal, and the penetration depth. For smooth shapes like spheres, the contact point is simply the closest point on each surface. Handling edge-edge collisions (e.g., two boxes meeting at their edges) requires careful clipping to avoid degenerate contacts that cause instability.

Execution: Building a Collision Response System

After detection, the next step is resolving collisions—moving objects apart and applying impulses to change their velocities. The most common approach is impulse-based resolution, which applies an instantaneous change in velocity at each contact point to satisfy the non-penetration constraint.

Impulse Resolution Basics

For each contact, you compute the relative velocity at the contact point, then apply an impulse along the contact normal to separate the objects. The impulse magnitude depends on the coefficient of restitution (bounciness) and the masses. For a simple bouncing ball, the impulse is straightforward. For multiple simultaneous contacts (e.g., a box resting on two edges), you must solve a system of constraints. The common solution is to use an iterative sequential impulse solver: process each contact one at a time, applying impulses, and repeat for several iterations. This approach is simple and converges well for most scenes. However, it can struggle with high-mass ratios or deep stacks, leading to jitter or slow convergence. To mitigate this, you can add warm starting (reusing impulses from the previous frame) and use a velocity-based solver that also accounts for friction.

Friction and Restitution

Friction is modeled using Coulomb's law: the tangential impulse is capped by the friction coefficient times the normal impulse. Implementing friction as part of the impulse solver requires clamping the tangential component. Many engines use a simple 'box friction' model that applies an impulse to stop relative tangential motion, then clamps it to the friction limit. This works well for most games but can cause 'sticky' friction at low velocities. More advanced models use an anisotropic friction or micro-slip model. Restitution (bounciness) is typically a scalar between 0 (perfectly inelastic) and 1 (perfectly elastic). In practice, values above 0.8 can cause instability, so many engines clamp restitution to a maximum of 0.9 or use a velocity-dependent model.

Stability Techniques

To prevent objects from sinking into each other or exploding, several stabilization techniques are used:

• Baumgarte Stabilization: Adds a small correction impulse proportional to the penetration depth. It's simple but can add energy to the system, causing jitter. Use a small factor (e.g., 0.2) to avoid overcorrection.
• Position-Based Dynamics (PBD): Instead of velocity-level resolution, PBD directly adjusts positions to satisfy constraints, then projects velocities accordingly. PBD is more stable for stacks and cloth but can look less physically accurate.
• Sleeping: When an object's velocity and angular velocity fall below thresholds for a few frames, it is marked as 'sleeping' and excluded from simulation until a collision wakes it. This dramatically improves performance for static piles.

In one composite scenario, a team building a physics puzzle game found that combining Baumgarte stabilization with a small slop region (where no correction is applied for tiny penetrations) eliminated jitter in their block-stacking puzzles.

Tools, Stack, and Maintenance Realities

Choosing the right tools and understanding maintenance overhead can make or break a custom physics engine project. While the engine itself is code, the surrounding ecosystem—debugging tools, profiling, and testing—is equally important.

Development Stack Choices

Most custom physics engines are written in C++ for performance, but other languages are viable depending on the use case. For a game engine, C++ with SIMD intrinsics (e.g., SSE, AVX) is common. For a simulation or research project, Python with NumPy can prototype algorithms quickly, though performance will be limited. Some teams use a hybrid approach: prototype in Python, then rewrite critical paths in C++ or use Cython. The math library is foundational; many developers roll their own vector and matrix classes, but using a well-tested library like GLM (OpenGL Mathematics) or Eigen can save time and reduce bugs. For spatial partitioning, implementing a simple grid is straightforward, but a BVH library like Intel's Embree can accelerate raycasting and collision queries.

Debugging and Visualization

Collision bugs are notoriously hard to debug because they occur in a fraction of a second. A good debugging workflow includes:

• Visual Debugging: Render bounding boxes, contact points, normals, and penetration depths as colored shapes. This lets you see if contacts are missing or misaligned.
• Step-by-Step Simulation: Allow the simulation to be advanced one frame at a time, with the ability to inspect object states (position, velocity, contact list).
• Logging: Log contact generation and resolution for each pair, then replay the log to isolate issues.

Many teams also write automated tests with known scenarios (e.g., a ball dropped on a plane, a stack of boxes) and compare the output to expected behavior. Regression tests are crucial because a fix for one bug often introduces another.

Maintenance Overhead

Maintaining a custom physics engine is a long-term commitment. As the engine evolves, you'll need to handle edge cases like floating-point precision errors, multi-threading, and integration with the rest of the game engine. Floating-point errors can cause objects to drift or miss collisions over time; using double precision for critical calculations or implementing periodic 'snapping' to grid can help. Multi-threading collision detection is challenging because it requires careful synchronization; a common pattern is to use a job system that processes broad-phase pairs in parallel, then resolves contacts sequentially to avoid race conditions. Integration with rendering and audio systems also requires careful design: collision events need to be communicated to other systems without introducing latency.

Growth Mechanics: Performance Tuning and Scaling

As your simulation grows, performance becomes the bottleneck. Scaling from a few dozen objects to thousands requires careful optimization and architectural decisions.

Profiling and Bottlenecks

The first step is profiling. Use a tool like Tracy, Optick, or built-in profilers to identify which phase (broad, narrow, or resolution) consumes the most time. In many engines, the narrow phase dominates because it performs expensive shape tests. Optimizations include:

• Early Exit: Use bounding sphere tests before full SAT or GJK. If bounding spheres don't overlap, skip the narrow test.
• Caching: Cache the separating axis from the previous frame (temporal coherence). If the same pair was separated by an axis last frame, test that axis first—it often still separates.
• SIMD: Vectorize narrow-phase tests using SIMD instructions. For example, testing four axes at once can give a 3-4x speedup.

Parallelism

Modern CPUs have many cores, so parallelizing collision detection is essential. The broad phase can be parallelized by dividing pairs among threads, but care must be taken to avoid false sharing. The narrow phase is embarrassingly parallel—each pair can be tested independently. However, the resolution phase is harder to parallelize because contacts interact. A common approach is to use a 'parallel narrow phase, sequential solver' pattern: detect collisions in parallel, then resolve them on a single thread or with a parallel solver that uses graph coloring to group independent contacts.

Memory Management

Collision detection generates a lot of temporary data: contact manifolds, broad-phase pairs, and spatial partitioning structures. Frequent allocations can cause cache misses and slowdowns. Use custom allocators (e.g., stack allocators, frame allocators) to reduce heap fragmentation. Pre-allocate arrays for pairs and contacts based on expected maximum counts, and reuse them each frame. For spatial hashing, a fixed-size grid with a free list of cells can avoid dynamic allocation.

Risks, Pitfalls, and Mitigations

Even experienced developers encounter common pitfalls when building a physics engine. Recognizing them early can save weeks of debugging.

The Jitter Problem

Jitter manifests as small, rapid oscillations in resting objects. It often results from overcorrection in Baumgarte stabilization or insufficient solver iterations. Mitigations include:

• Contact Accumulation: Accumulate impulses over multiple frames (warm starting) to reduce the need for large corrections.
• NGS (Nonlinear Gauss-Seidel) Solver: Use a more accurate solver that considers contact ordering, though it's more complex.
• Damping: Add a small amount of velocity damping to high-frequency oscillations, but be careful not to dampen natural motion.

The Ghost Collision

Ghost collisions occur when a contact is generated incorrectly, causing objects to bounce off thin air. This often happens with concave shapes or when contact normals are flipped. Using robust convex decomposition and verifying contact normals via the closest points can help. Also, ensure that your narrow-phase test returns consistent normals (e.g., always from object A to B).

Performance Spikes

When many objects collide simultaneously (e.g., an explosion), the number of contact pairs can spike, causing a frame drop. To mitigate, you can limit the number of contacts processed per frame, use a time budget that stops detection after a threshold, or implement a 'sleeping' system that reduces active objects. Another approach is to use a 'contact reduction' algorithm that merges nearby contacts into a single representative contact, reducing solver load.

Mini-FAQ and Decision Checklist

Frequently Asked Questions

Q: Should I use an existing engine or build my own? A: If your project requires unique behavior (e.g., non-Newtonian physics, custom constraints) or you need to learn the fundamentals, building your own is worthwhile. For most commercial games, using an established engine is faster and more reliable.

Q: How do I handle concave shapes? A: Decompose them into convex parts using a tool like V-HACD, or use a mesh collider that tests against triangles individually (expensive but simpler).

Q: What's the best broad-phase algorithm? A: It depends on your scene. For small to medium scenes with uniform object sizes, sweep and prune is simple and fast. For large, sparse scenes with varying sizes, a BVH is more efficient.

Q: Why do my objects sink into the floor? A: This usually indicates insufficient solver iterations, too large a time step, or a bug in contact generation (e.g., wrong normal direction). Increase iterations, reduce time step, or check your contact manifold.

Decision Checklist

Before committing to a custom physics engine, consider:

• Do you need real-time performance? (Yes: C++ with SIMD; No: Python may suffice)
• Will you have many concave objects? (Yes: plan for convex decomposition)
• Is stability more important than physical accuracy? (Yes: consider PBD)
• Do you have time for long-term maintenance? (Yes: custom engine is viable; No: use an existing library)

Synthesis and Next Steps

Building a custom physics engine is a deep, rewarding challenge that teaches you the fundamentals of simulation and problem-solving. The key to success is starting simple: implement a basic rigid-body engine with sphere-plane collisions first, then gradually add box-box, convex hulls, and CCD. Each step will reveal new challenges—tunneling, stacking, jitter—that you can solve incrementally. Use the techniques outlined in this guide: broad and narrow phase detection, impulse-based resolution with iterative solvers, and stabilization methods like Baumgarte or PBD. Remember to invest in debugging tools and automated tests early; they will save you countless hours. Finally, consider whether a custom engine truly serves your project's needs. If you decide to proceed, start with a small prototype and iterate. The knowledge you gain will be invaluable, even if you eventually switch to an existing engine.