Case study: 3D placement pipeline

A 3D placement pipeline from Blender to render farm

A product placement workflow that moves from a browser plan to verified Blender scenes and repeatable renders.

Try the live demo30 seconds · representative sample data · nothing leaves your browser

Client work2025–2026In production

Project details

Year: 2025–2026
Role: Plugin · engine · infrastructure
Status: In production
Stack: Blender 4.2 Extension API · Python · FastAPI · AWS ECS Fargate · Redis · Cloudflare R2
Relationship: Selected delivery work; the public case uses a functional project name.
Market: 3D interior-production workflow; client identity is not disclosed publicly.

Try it

Try the placement workflow.

Drag the slots around the room. The demo applies the same placement rules and shows the structured export used to prepare a scene.

Interactive demo, using representative data in your browser

drag to move · tap to select · arrow keys to nudge, bounds validation is the plugin's real AABB logic

Live export · the engine's real input format

{
 "apartment": "demo_studio_01",
 "designer": "zawish.si visitor",
 "slots": [
  {
   "slot_id": "slot_room1a_Bed_Generic_01",
   "usage_class": "Bed_Generic",
   "sku": "BED-1042",
   "pivot": "bottom",
   "position_m": [
    1.1,
    1.35
   ],
   "dims_min": [
    1.1065,
    0.0762,
    1.27
   ],
   "dims_max": [
    1.6256,
    2.1336,
    1.4478
   ],
   "valid": true
  },
  {
   "slot_id": "slot_room1a_Seating_Large_02",
   "usage_class": "Seating_Large",
   "sku": "SOF-2210",
   "pivot": "bottom",
   "position_m": [
    4.2,
    3.8
   ],
   "dims_min": [
    1.2192,
    0.5588,
    0.6096
   ],
   "dims_max": [
    2.8448,
    1.6766,
    0.9131
   ],
   "valid": true
  },
  {
   "slot_id": "slot_room1a_Surface_Low_03",
   "usage_class": "Surface_Low",
   "sku": "TBL-0870",
   "pivot": "bottom",
   "position_m": [
    4.2,
    2.5
   ],
   "dims_min": [
    1.2192,
    0.508,
    0.4582
   ],
   "dims_max": [
    1.2192,
    0.508,
    0.4582
   ],
   "valid": true
  },
  {
   "slot_id": "slot_room1a_Decor_Rug_04",
   "usage_class": "Decor_Rug",
   "sku": "RUG-3301",
   "pivot": "bottom",
   "position_m": [
    4.2,
    2.9
   ],
   "dims_min": [
    0.6096,
    0.9144,
    0.0254
   ],
   "dims_max": [
    3.048,
    4.2672,
    0.0254
   ],
   "valid": true
  },
  {
   "slot_id": "slot_room1a_Lighting_Tabletop_05",
   "usage_class": "Lighting_Tabletop",
   "sku": "LMP-0114",
   "pivot": "tabletop",
   "position_m": [
    4.2,
    2.5
   ],
   "dims_min": [
    0.3709,
    0.2127,
    0.5153
   ],
   "dims_max": [
    0.5066,
    0.3709,
    0.5564
   ],
   "valid": true
  }
 ]
}

The problem

From a browser floor plan to a checked Blender scene and repeatable render.

Repeated room staging becomes expensive when every layout is rebuilt by hand. The pipeline needed to place real product bundles, catch invalid layouts, and prepare scenes for rendering.

ZAWISH made a placement interface, structured scene export, Blender automation, product metadata rules, validation, and render-job preparation.

The system

How it fits together.

Scroll sideways to explore the enlarged map.

Read the components

The Blender plugin and scene export provide placement inputs. The placement engine connects to the render queue, GPU rendering, and delivery storage.

Blender plugin: smart slots · SKUs
Scene export: GLB + JSON
Placement engine: catalog match
Render queue: Redis
GPU render farm: ECS Fargate
Delivery: R2 presigned

simplified public map of implemented components and boundaries

The build

Validate the scene before expensive work begins.

Smart slots instead of meshes

Inside Blender, designers place MetaFurniture bundles, a metadata anchor, a proxy shape, and min/max bounding boxes per usage class. A bed slot knows it's a bed, what sizes are acceptable, and which room volume it sits in (point-in-bounds AABB detection).

Blender 4.2 · N-panel UI

A placement engine that shops the catalog

The exported JSON + GLB feed an engine that matches each slot against a 257-product library across 42 usage classes, filtering by category, fitting by bounds, and loading the winning model into the scene.

FastAPI · GLB loader · raycasting

Queued rendering without idle workers

Finished scenes queue into serverless GPU containers that spin up per job, run Blender headless, upload frames to storage, and terminate. This keeps interactive preparation separate from compute-heavy rendering.

ECS Fargate · Redis · R2

Validation before anything renders

Scale checks, collision checks, and SKU verification run at export, a bad scene fails in the designer's viewport in seconds, not on a GPU thirty minutes later. The demo above enforces the same rule.

export gate · schema validation

Implementation evidence

Usage classesplacement intent is expressed independently of individual products

GLB + JSONgeometry and placement intent travel as a small explicit contract

Queued workersremote rendering is isolated from interactive scene preparation

Constraint

Scene intent had to survive the handoff from an artist tool to a catalog matcher and remote render workers.

Tradeoff

A small, explicit GLB and JSON contract is used instead of coupling Blender files directly to the rendering service.

The public demo shows the placement contract and export behavior with representative products; operational volume is intentionally not claimed.

For your business

Discuss a related system.

The public placement demo shows the interaction and export structure. Production asset libraries and render infrastructure remain private.

Start a project More work

Next caseA production suite for AI campaign visuals →