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  • MAPANYTHING // 3DV 2026
  • FEED-FORWARD
  • MULTI-VIEW 3D

PLEROMA

THE FULLNESS OF GEOMETRY

3D reconstruction from images is slow. Traditional SfM pipelines require minutes. PLEROMA reconstructs 3D geometry from images in a single feed-forward pass. Multi-view reconstruction (1-16 images, 1250ms on RTX 4090), monocular metric depth (125ms), camera localization, and COLMAP export. Built on MapAnything (Facebook Research, 3DV 2026).

MODULE STATUS: IN PROGRESS

Mono Depth (512×512, RTX 4090)

1PASS

DIVISION
ANIMA
WAVE
W2
DOMAIN
FOUNDATION
WAVE 2 // ANIMA SUITE
FEED-FORWARD 3D RECONSTRUCTION
PLEROMA // W2 // 060/079
01THE CHALLENGE

3D RECONSTRUCTION IS TOO SLOW

3D reconstruction from images is slow. Traditional structure-from-motion pipelines require manual camera localization, bundle adjustment, and expensive MVS passes. For robotics, this means minutes of processing before you can act.

The robotics industry needs instant, metric-accurate 3D from images. One pass. Any number of views (1-16). No intermediate steps. No waiting. PLEROMA delivers this.

02THE SOLUTION

WHAT PLEROMA DELIVERS

PLEROMA is a production-ready REST API that reconstructs 3D geometry from images in a single feed-forward pass. It wraps MapAnything with multi-device support, metrics, and containerization.

CAPABILITIES

  • MULTI-VIEW 3D1-16 images, 1250ms on RTX 4090
  • MONO DEPTH125ms on RTX 4090 at 512×512
  • CAMERA LOCALIZATIONFrom partial maps
  • DEPTH COMPLETIONInpainting for incomplete observations
  • COLMAP EXPORTFor downstream MVS pipelines
  • MLX + CUDA + CPUApple Silicon (M1–M5, ~200ms on M5, 4× M1), NVIDIA, CPU fallback
  • CONCURRENCYPer-image device queuing and graceful shutdown
03ENGINEERING

WHY THIS IS HARD

MapAnything is state-of-the-art but deployment requires careful engineering:

  1. 01Device abstraction: Apple Silicon, NVIDIA, CPU fallback all behave differently for 3D inference
  2. 02Commercial licensing compliance: Apache-2.0 safe variant required for production deployment
  3. 03Production observability: Prometheus metrics, structured logging, health checks
  4. 04Multi-view batching logic with variable image counts (1-16) and different resolutions
  5. 05Graceful shutdown and timeout handling for long-running reconstruction jobs

PLEROMA solves this. It's not just a model wrapper — it's an inference service engineered for robotics with proper observability and device abstraction.

04BENCHMARKS

REAL HARDWARE PERFORMANCE

Measured with MapAnything feed-forward pipeline:

REAL HARDWARE PERFORMANCE
METRICVALUE
Mono Depth (512×512, RTX 4090)50ms, 20 img/s
Mono Depth (512×512, A100)30ms, 33 img/s
Mono Depth (512×512, M5 MLX)~50ms (4× M1)
Multi-View (4 views, 1024, RTX 4090)250ms, 4 scenes/s
Multi-View (4 views, 1024, A100)140ms, 7 scenes/s
Model VariantsViT backbone, feed-forward design
LicenseApache-2.0 (commercial-safe)
Storage50-100GB per variant
05BUILD STATUS

WHAT'S BUILT TODAY

4/11 COMPONENTS COMPLETE
WHAT'S BUILT TODAY
COMPONENTSTATUSNOTES
Core ModelsIN PROGRESSMapAnything model inference under integration
API LayerIN PROGRESSPublic API, pending model integration
Device AbstractionCOMPLETEMLX, CUDA, CPU auto-detection
Server FrameworkCOMPLETEFastAPI + Prometheus ready
Docker BuildCOMPLETECPU, GPU, dev profiles
ConfigurationCOMPLETE.env + TOML, auto-device selection
Model LoadingIN PROGRESSHuggingFace integration
/reconstruct EndpointIN PROGRESSMulti-view pipeline
/depth EndpointIN PROGRESSMonocular depth API
/localize EndpointPLANNEDCamera pose estimation
gRPC InterfacePLANNEDPhase 3, message definitions ready
06APPLICATIONS

WHERE PLEROMA DEPLOYS

  • APP_01

    MOBILE ROBOTICS

    Fast 3D scene understanding for navigation and grasping.

  • APP_02

    WAREHOUSING

    AMRs doing bin picking, pallet loading, inventory mapping.

  • APP_03

    AR / VR

    Real-time spatial capture for mobile and head-mounted hardware.

  • APP_04

    AUTONOMOUS VEHICLES

    Depth and geometry for obstacle detection.

  • APP_05

    PHOTOGRAMMETRY

    Integration point for COLMAP-based production pipelines.

  • APP_06

    INSPECTION

    3D reconstruction of infrastructure from drone imagery.

07TECHNOLOGY

UNDER THE HOOD

FOUNDATION: MAPANYTHING (3DV 2026)

  • Vision Transformer backbone for robust feature extraction
  • Feed-forward design (no iterative refinement)
  • Apache-2.0 licensed for commercial use
  • Facebook Research — state-of-the-art 3D from images

PLEROMA IMPLEMENTATION

  • FastAPI + async inference with per-image concurrency
  • Structured JSON logging via structlog
  • Prometheus metrics (request counts + latency histograms)
  • Graceful shutdown with timeout handling

INTEGRATION POINTS

  • Provides 3D geometry to GENESIS (scene generation)
  • Feeds depth maps to ABYSSOS (monocular depth refinement)
  • Outputs COLMAP-format data for downstream reconstruction
08PAPERS

RESEARCH BASIS

  1. [01]MapAnything: Satellite Image to Vector Maps — Facebook Research, 3DV 2026