Vortex-Depth-V9-Corridor (Lighthouse)

A 5.31 × 10⁶ parameter monocular depth + 6-class segmentation student specialized for corridor-class indoor environments. The production corridor checkpoint of the Vortex-Depth lineage. Closed-loop validated against ground-truth depth in simulation.

Headline result

LILocBench corridor RMSE: 0.382 m — the lineage's best corridor depth measurement.

Closed-loop navigation parity with ground-truth depth in Gazebo Fortress corridor experiments:

The single failure is shared between configurations (identical seed, equivalent final pose error), indicating a planner-level failure mode rather than a depth-quality difference.

Use case

Recommended for fixed-route corridor deployment in indoor robot navigation. The intended deployment context is corridors with the structural characteristics of typical institutional indoor spaces: long parallel walls, mid-field-dominant depth distributions (1–15 m), uniform or fluorescent lighting. The model has been fine-tuned on the LILocBench Bonn corridor dataset and achieves competitive corridor depth metrics on that distribution.

For environments outside the corridor distribution, see:

• vortex-depth-v5-general (Atlas) — general-purpose indoor depth across diverse room geometries
• vortex-depth-v6-pretrained (Cornerstone) — fine-tuning base for additional corridor-class specialists

Operational limitations

V9 is specialized for corridor environments. Predictable performance regressions occur under the following conditions:

Loading

import torch
from models.student import build_student
from config import Config

cfg = Config()
model = build_student(num_classes=cfg.NUM_CLASSES, pretrained=False, backbone=cfg.BACKBONE)
state = torch.load("best_depth_v9.pt", map_location="cpu")
model.load_state_dict(state)
model.eval()

with torch.no_grad():
    depth, seg_logits = model(rgb_tensor)  # rgb_tensor: [B, 3, 240, 320]

Deployment realization

At runtime, V9 depth is consumed by a confidence-gated fusion node combining ToF and student outputs per pixel:

s = median(d_tof / d_v9)        over pixels where ToF confidence ≥ 0.5

if confidence(i, j) ≥ 0.5 AND 0.05 m < tof_depth(i, j) < 10.0 m:
    fused(i, j) = tof_depth(i, j)            # use hardware reading directly
else:
    fused(i, j) = s · v9_depth(i, j)         # use scaled learned depth

The per-frame median-scale step anchors the V9 prediction to metric units using the surviving ToF pixels. The model is therefore not required to produce absolute scale accuracy — it must produce accurate depth structure, which the runtime then anchors per frame.

Training

Fine-tuned from the V6 multi-domain pretrained checkpoint on LILocBench dynamics_0 for 50 epochs. AdamW optimizer, encoder LR 3 × 10⁻⁵, decoder LR 3 × 10⁻⁴, cosine annealing, batch size 16. Loss formulation unchanged from earlier lineage stages: berHu (depth) + cross-entropy (segmentation) + edge-aware smoothness, Kendall-weighted with log σ² clamped to [-2, 2].

The complete training pipeline that produces V9 spans four sequential stages:

1. ImageNet pretraining (timm default, ~1.3 × 10⁶ classification labels).
2. Multi-domain pretraining on SUN RGB-D + DIODE Indoor + NYU Depth V2 (V6 pretrain checkpoint).
3. NYU Depth V2 fine-tuning with augmentation pipeline (V6 final checkpoint).
4. LILocBench dynamics_0 fine-tuning (V9 final checkpoint).

The first three stages are inherited from V6; only the final corridor-specialization stage is unique to V9.

Bootstrap perception context

This checkpoint is the production corridor model in a three-checkpoint family released as part of the Vortex bootstrap-perception pipeline. The pipeline addresses the operational reality that Time-of-Flight depth sensors lose ~78 % of their pixels on reflective indoor surfaces (polished floors, glass walls); the student fills the dead pixels with consistent learned geometry, and runtime fusion combines surviving sensor pixels with the student output.

Project resources

• Codebase: github.com/Nishant-ZFYII/ml_inference
• Documentation: nishant-zfyii.github.io/ml_inference
• V9 model page: Lighthouse (V9)

Reference

If you use this model in your work, please reference the project repository:

https://github.com/Nishant-ZFYII/ml_inference

License

MIT.