Object tracking delay causing inaccurate overlays

The Impact of Latency on Mixed Reality Experiences

Even small tracking delays create noticeable problems:

• 50ms lag causes 3cm displacement at normal movement speeds
• 100ms+ delay makes virtual objects feel “draggy” and disconnected
• Inconsistent latency destroys the illusion of object permanence
• Cumulative errors lead to drift over time

Root Causes of Tracking Latency

1. Sensor Pipeline Delays

Sensor Type	Typical Latency	Primary Bottlenecks
RGB Camera	80-120ms	Exposure, ISP processing
IR Depth Sensors	50-80ms	Depth calculation
LiDAR	30-50ms	Point cloud generation
IMU	5-10ms	Sensor fusion

2. Processing Chain Breakdown

1. Capture latency (sensor readout)
2. Feature extraction (CV algorithms)
3. Pose estimation (PnP solving)
4. Rendering queue (frame buffering)
5. Display pipeline (scanout)

3. Common System Architecture Flaws

• Synchronous processing instead of pipelining
• CPU-bound CV tasks blocking GPU work
• Insufficient pose prediction

Technical Solutions for Low-Latency Tracking

1. Hardware-Accelerated Tracking

// Example using Qualcomm's FastCV
void processFrame(FastCVImage* frame) {
    fcvCornerDetectFASTu8(frame, corners);
    fcvTrackLKOpticalFlow(prevFrame, frame, corners);
    fcvSolvePnP(corners, objectModel, &pose);

    // Total processing: <8ms on Snapdragon XR2
}

2. Predictive Tracking Algorithms

# Kalman filter for pose prediction
class Tracker:
    def predict(self):
        dt = time_since_last_update()
        self.x += self.v * dt + 0.5*self.a*dt**2
        self.P += self.Q # Process noise

    def update(self, measurement):
        # Standard Kalman update
        K = self.P @ self.H.T @ np.linalg.inv(self.H @ self.P @ self.H.T + self.R)
        self.x += K @ (measurement - self.H @ self.x)
        self.P = (I - K @ self.H) @ self.P

3. Pipeline Optimization

Stage	Optimization	Latency Reduction
Capture	Rolling shutter correction	15ms
Processing	GPU-accelerated CV	30ms
Rendering	Late latching	11ms
Display	Direct scanout	8ms

Platform-Specific Implementations

ARKit/ARCore Best Practices

// Configure for low latency
let config = ARWorldTrackingConfiguration()
config.isAutoFocusEnabled = false // Reduces exposure changes
config.minimumNumberOfTrackedImages = 1 // Faster initialization
session.run(config, options: [.resetTracking])

Unity DOTS Approach

// Burst-compiled tracking system
[BurstCompile]
public struct TrackingJob : IJobParallelFor {
    public NativeArray<FeaturePoint> points;
    public NativeArray<Pose> poses;

    public void Execute(int index) {
        // SIMD-optimized tracking
    }
}

Visual Compensation Techniques

1. Warping and Reprojection

• Timewarp for rotational compensation
• Positional warping based on latest IMU
• Asynchronous reprojection (Oculus)

2. Artistic Mitigations

• Motion blur matching real movement
• Trail effects during fast motion
• Soft edges on dynamic overlays

Debugging and Measurement

1. Latency Measurement Tools

Method	Accuracy	Setup Complexity
Photodiode testing	±1ms	High
High-speed video	±5ms	Medium
SDK profiling	±10ms	Low

2. Key Metrics to Monitor

• End-to-end latency (capture to photons)
• Pose update jitter
• Tracking confidence scores
• CPU/GPU pipeline utilization

Emerging Solutions

1. Neural Tracking

• CNN-based pose estimation (5ms inference)
• Temporal coherence networks
• Attention-based feature selection

2. Edge Computing

• Offloading tracking to companion device
• Distributed SLAM processing
• Cloud-assisted localization

3. Sensor Fusion Advancements

• 6DoF IMU prediction
• Event camera integration
• Millimeter-wave radar

Case Study: Industrial AR Maintenance

A field service solution achieved <20ms overlay latency by:

• Custom FPGA-based image processing
• IMU-forward prediction
• Local CAD model caching
• Asynchronous timewarp

Best Practices Checklist

✓ Profile each pipeline stage
✓ Implement hardware acceleration
✓ Add predictive tracking
✓ Optimize render timing
✓ Provide visual fallbacks