Lumina Spatial Gallery Orchestration

The session utilized a high-fidelity **Orchestrator-Worker** pattern. The Lead Architect (Pro) handled non-linear planning, research, and technical verification, while Specialized Workers (Flash) handled the sequential build of the 3D module set.

• PRD Audit: Analyzed the visionOS benchmark to define the 16-bit depth standard.
• Safety/Logic: Ensured zero foveation rules were injected into the WebXR initialization to maintain desktop-to-headset parity.

Constructed a local Python service to bridge the gap between 2D images and 3D spatial scenes.

# depth_service.py Inference Pipeline
pipeline = pipeline("depth-estimation", model="Depth-Anything-V2-Small-hf")
result = pipeline(image)
depth_array = (depth_array * 65535).astype(np.uint16) # 16-bit Precision
                

Result: Successfully generated `depth.png` for cinematic spatial photo displacement.

Implemented a custom GLSL shader injected into `THREE.MeshStandardMaterial` for real-time vertex displacement.

• Vertex: Displace Z-position based on R-channel of the displacementMap.
• Fragment: Implemented a circular/rectangular smoothstep alpha feathering range `[0.95, 1.0]` to eliminate hard geometric edges in AR.

Implemented the `HandTrackingManager` to monitor 25 joint positions in real-time.

• Gesture: Pinch detection via Euclidean distance between Joint 4 (THUMB_TIP) and Joint 9 (INDEX_TIP).
• Haptics: Automated 20ms pulse trigger on gesture activation for tactile confirmation.

Automated visual testing of the deployment across 3 diagnostic cycles.

• Cycle 1: Initial framework validation.
• Cycle 2: Identified critical module race condition (ReferenceError: THREE not defined).
• Cycle 3: Final Alpha verification with Desktop Mode bypass enabling visual confirmation of 3D displacement.

Most spatial web viewers use 8-bit depth (255 levels), resulting in visible "stepping" or "aliasing" on large 3D planes. Lumina enforces a 16-bit (65,535 levels) standard. By using `np.uint16` in the ML service and `FloatType` textures where available, we ensure the diorama feels optically continuous, effectively matching the depth fidelity of the visionOS Photos ecosystem.

The close parity between **Realtime Duration (140m)** and **Compute Time (135m)** in this session reveals a deliberate **Serial-Orchestrated** build strategy. While the cluster utilized multiple agent tiers, the Foundation Phase (1-5) was intentionally hardware-locked and dependency-sequential.

• Architecture-Locked: The volumetric renderer required stable GLSL shaders before the PhotoCard mesh could be generated.
• Hardware-Locked: Verification of skeletal tracking loops required waiting for Subagent-rendered Chrome contexts.
• Strategic Choice: A "zero-error" architectural foundation was prioritized over raw parallel throughput to ensure the the engine's core stability.

Next Phase (Parallel): True parallelism is projected for Phase 6: Multi-Asset Ingestion, where batch depth generation, storage, and QA will be decentralized across parallel worker clusters.