NeRF-Based 3D Reconstruction Turnkey Development

NeRF-Based 3D Reconstruction Development

You have re-shot an object from 50 angles, but after photogrammetry you get a model with holes and blurry textures. Standard algorithms fail on complex geometry and glossy surfaces. NeRF solves this by encoding the scene into neural network weights — no mesh, no skepticism about reflections. We develop such systems turnkey. Our portfolio includes projects from artifact reconstruction to VR tours of architectural objects. Over 10 years of experience. We guarantee transparent results: PSNR > 30 dB on test views. Recently, we reconstructed an interior hall for a client — 200 photos, indoor scene 20×15 m. We used Mip-NeRF 360 on A100 (4 hours training). Got a mesh with 2 mm point detail. Result: a VR tour with realistic lighting. Typical problems — low image quality, uneven lighting, dynamic objects (people, shadows). We know how to bypass them: masking, HDR shooting, synthesis of missing angles.

How to choose the method? Instant-NGP vs Nerfacto vs Mip-NeRF 360

Instant-NGP is 6x faster than Nerfacto, but Nerfacto gives better detail on indoor scenes. For outdoor scenes with a large depth range, Mip-NeRF 360 is the number one choice. Method comparison:

Nerfstudio: modern framework

# Installation and run via nerfstudio
# pip install nerfstudio

from nerfstudio.configs.method_configs import method_configs
from nerfstudio.engine.trainer import TrainerConfig

# Method selection
config = method_configs['nerfacto']  # neural context = good balance

config.pipeline.model.near_plane = 0.1
config.pipeline.model.far_plane = 1000.0
config.max_num_iterations = 30000

# CLI:
# ns-train nerfacto --data /path/to/images
# ns-render --load-config outputs/exp/nerfacto/config.yml \
#            --traj interpolate --output-path render.mp4

Instant-NGP: fast NeRF

# instant-ngp trains in minutes thanks to hash grid encoding
# Python binding:
import pyngp

testbed = pyngp.Testbed(pyngp.TestbedMode.Nerf)
testbed.load_training_data('transforms.json')  # COLMAP/nerfstudio format

testbed.nerf.training.near_distance = 0.01
testbed.train(max_iterations=5000)

# Synthesis of a new viewpoint
testbed.camera_matrix = look_at(eye=[0, 0, 2], target=[0, 0, 0])
frame = testbed.render(width=1920, height=1080, spp=8)

Data Preparation: COLMAP preprocessing

NeRF requires accurate camera poses. The standard path is COLMAP SfM:

# From photos → poses in nerfstudio format
ns-process-data images \
  --data ./photos \
  --output-dir ./processed \
  --sfm-tool colmap \
  --matching-method exhaustive

transforms.json — the output file with camera intrinsics and transformation matrices for each frame.

What to do if the scene contains dynamic objects?

Dynamic objects (people, cars) — standard NeRF does not work with them. Solution: dynamic masking (MaskNeRF) or decomposition into s-t time field (D-NeRF). For scenes with motion, we apply dynamic NeRF variants, which allows obtaining a quality result even with moving elements.

Work process: from photos to ready model

1. Task analysis: determine scene type, required number of angles, necessary resolution.
2. Data collection and preprocessing: we perform photography with calibrated equipment, COLMAP to extract camera poses.
3. Model training: select the optimal method (Nerfacto/Instant-NGP/Mip-NeRF) and architecture, train on GPU (A100/RTX4090).
4. Validation: evaluate PSNR, SSIM, LPIPS on test split. Achieve PSNR > 30 dB.
5. Geometry export: extract mesh using Marching Cubes with resolution up to 2048³, export to PLY/OBJ.
6. Post-processing and integration: retopology, texturing, preparation for VR/AR or web viewer.
7. Documentation and training: hand over metrics, run instructions, train your specialist (2-3 days).

Timelines: from 3 weeks for an object to 8 weeks for a complex scene. Contact us — we will assess your project within 1-2 business days.

What is included in the work

• Photogrammetric shooting (on-site or using your data)
• Preprocessing (COLMAP, masking)
• Training and validation of NeRF model
• Mesh export in PLY, OBJ, FBX formats
• Documentation with metrics and reproduction guide
• Training your specialist to work with the model
• Support for 3 months

NeRF limitations and workarounds

Mirrors and transparent glass are difficult for NeRF. Ref-NeRF helps by modeling reflections separately. For city-scale scenes (kilometer range), we use Block-NeRF or Mega-NeRF. All these techniques we adapt to your project.

Exporting 3D geometry

NeRF stores the scene in network weights, but for use in 3D editors a mesh is needed:

# Extract mesh from NeRF via Marching Cubes
from nerfstudio.exporter.exporter_utils import generate_point_cloud
from nerfstudio.exporter.marching_cubes import generate_mesh_with_multires_marching_cubes

mesh = generate_mesh_with_multires_marching_cubes(
    pipeline=trainer.pipeline,
    resolution=2048,
    bounding_box_min=(-2, -2, -2),
    bounding_box_max=(2, 2, 2),
    isosurface_threshold=0.0,
    output_path=Path('output.ply')
)

Why order NeRF from us

• 10+ years of experience in computer vision and neural representations
• 5 completed NeRF projects (from museum exhibits to industrial workshops)
• Quality guarantee by metrics (PSNR, LPIPS)
• Proprietary developments: fast Instant-NGP with custom hash grid, Mip-NeRF 360 with adaptive bounding box
• Full cycle: from on-site shooting to delivery of a ready 3D model turnkey

Contact us for a consultation. We will assess your project within 1-2 business days.

According to the original article by Mildenhall et al., NeRF provides unprecedented quality of novel view synthesis. More about the technology on Wikipedia.

How Distribution Shift Kills CV Model Metrics in Industry

On a production line, a camera is installed to control product quality. The model is trained on 10,000 labeled images—test accuracy mAP 0.84. Deployed to production, and in the first week it misses 30% of defects. Lighting on the line changes between shifts; distribution shift nullifies the metrics. This is a classic story with computer vision in industry, where pattern recognition fails without proper drift handling.

Our engineers, with experience from 60+ computer vision projects, know how to eliminate such scenarios. We guarantee stable model performance under real conditions.

Object Detection: YOLO, RT-DETR, and Everything in Between

YOLO is the standard for real-time detection. YOLOv8 and YOLOv11 from Ultralytics are the most used versions in production: simple API, active community, built-in validation, and export to ONNX/TensorRT. For tasks with high accuracy requirements and less critical latency, RT-DETR, a transformer-based architecture without NMS, gives better mAP on COCO at comparable speed to YOLOv8l.

A typical mistake when training a detector: dataset of 8000 images, 3 classes, fine-tune YOLOv8m—F1 0.73 on validation. Look at confusion matrix—one class is almost never detected. Cause: imbalance 1:23. Solution: oversampling rare class, focal loss for objectness, augmentations (Mosaic, MixUp disabled for rare class as they "blur" it). Transfer learning is mandatory: pretrained on COCO weights reduces data requirement by 10 times. Fine-tuning on 500–2000 domain images yields a working model in 1–2 days on a single GPU.

For edge deployment: export to ONNX → TensorRT engine. YOLOv8n in TensorRT FP16 on Jetson AGX Orin gives 150+ FPS at P99 latency < 8 ms—3 times faster than ONNX Runtime without TensorRT. On server A10G: 700+ FPS for YOLOv8n in TensorRT INT8.

How Does Fine-Tuning YOLO Help in Pattern Recognition?

Suppose you need to find micro-defects on a metal surface—a task with high resolution and class imbalance. We use YOLOv8m pretrained on COCO and fine-tune on 2000 proprietary images. Apply augmentations Mosaic, MixUp, random perspective. After 200 epochs, mAP 0.5 reaches 0.93. Key techniques:

• Focal loss for the objectness head—reduces contribution of easily classified examples.
• Class-balanced sampling—equalizes representation of rare classes.
• Test Time Augmentation (TTA)—increases recall by 5–7% through averaging over flips and scales.

Get a consultation on architecture selection for your task—contact us.

Segmentation: SAM, Mask R-CNN, and Instance Segmentation

SAM (Segment Anything Model) from Meta changed the approach to segmentation. SAM 2 works with video, supports object tracking across frames—for interactive object selection by point or bbox, it's the best out-of-the-box choice. For production instance segmentation without interactive prompting, Mask R-CNN or YOLOv8-seg are used. YOLOv8-seg trains like a regular detector with additional masks, convenient in the same pipelines. Semantic segmentation (each pixel is a class) uses SegFormer, DeepLabV3+. SegFormer-B5 provides a good balance of accuracy and speed for satellite imagery or medical segmentation.

Case study: cell segmentation on microscopic images. Dataset of 400 images with manual annotation. Training Mask R-CNN on ResNet-50 backbone gave IoU 0.61—poor. Problem: objects (cells) overlap; standard NMS kills overlapping predictions. Solution: switch to cellpose (specialized architecture for biomedical tasks) + soft-NMS. IoU increased to 0.79.

OCR: When Tesseract Fails

Tesseract is a starting point for simple tasks: printed text, good lighting, straight layout. As soon as there are handwritten elements, non-standard fonts, perspective distortions, or multi-column layouts, Tesseract degrades quickly.

PaddleOCR is a production-grade solution: text block detection + recognition + structural analysis. Works out of the box for 80+ languages, including Russian. Supports tables and complex document structures. TrOCR (Microsoft) is a transformer OCR with strong results on handwritten text. For Russian handwritten text, fine-tuning is needed: the base model is trained mostly on Latin script.

What to Do When Tesseract Cannot Handle Pattern Recognition on Documents?

For tasks like "extract data from invoices/contracts/passports," we use LayoutLMv3 or Donut—these models understand document layout, not just text. Integration via Hugging Face Transformers, fine-tuning on 200–500 annotated documents. Typical pipeline:

1. Preprocessing: deskew, denoising, binarization via OpenCV.
2. Text block detection: PaddleOCR detection or CRAFT.
3. Recognition: PaddleOCR recognition or TrOCR.
4. Post-processing: normalization, validation via regex or LLM for structured fields.

For documents with fixed structure, template matching + OCR by coordinates is often more reliable than an end-to-end solution.

Face Recognition: Identification and Verification

Face recognition = detection + alignment + embedding + matching. Each stage matters.

Detection: RetinaFace or InsightFace for accurate face localization and keypoints. MTCNN is older but reliable. Embedding: ArcFace (InsightFace) is state-of-the-art for face recognition embeddings. Models iresnet50/iresnet100 pretrained on MS1MV3 (5M identities). Embedding vector 512 float32, comparison by cosine similarity. Threshold tuning: decision threshold is a critical parameter. At threshold 0.6, typical FPR on LFW benchmark is 0.001, TPR is 0.985. In production, threshold must be calibrated to the real distribution: people in masks, with changed appearance, different lighting conditions. Liveness detection is mandatory: MiniFASNet—lightweight model on CPU; FaceX-Zoo contains several pretrained liveness detectors.

Video Analytics

Video is a sequence of frames plus a temporal dimension. A naive approach—detecting on every frame—is expensive.

Tracking: ByteTrack and BoT-SORT are the standard for multi-object tracking. They work on top of any detector, adding persistent IDs to objects across frames—enabling object counting, motion tracking, velocity.

Optimization: not every frame needs processing. For static scenes, detect every 5–10 frames, with tracking in between. For event detection (person entering a zone), background subtraction (OpenCV MOG2) serves as a lightweight pre-filter before neural detection. Action recognition: SlowFast, VideoMAE for action classification. Heavy models—for production use ONNX export + TensorRT or offline processing.

How to Measure Pattern Recognition Model Quality in Production?

Quality monitoring is key to MLOps. We track:

• Prediction confidence distribution.
• Share of low-confidence predictions (indicator of OOD data).
• Drift of input images via feature distribution (embeddings from backbone).

A drop in average confidence from 0.87 to 0.71 over a week is an early signal of distribution shift. NVIDIA Triton Inference Server recommends tracking these metrics via Prometheus. Our certified engineers set up monitoring and guarantee SLA for inference quality.

Deployment of CV Models

For online inference, we use Triton Inference Server (NVIDIA)—production standard for serving CV models. Supports TensorRT, ONNX, PyTorch, dynamic batching, multiple instances. REST and gRPC API. We guarantee stable operation under load.

Edge deployment: ONNX Runtime on ARM/x86 CPU. TensorFlow Lite for mobile devices. OpenVINO for Intel CPU/GPU/VPU—gives 2–3× speedup on Intel hardware compared to ONNX Runtime. After deployment, we hand over the model with documentation and train personnel.

What Is Included in the Work

Deadlines and Cost

A prototype detector on existing data takes 1–2 weeks. Production system with optimization for target hardware takes 4–8 weeks. Full cycle including data labeling (1000–5000 images) takes 2–4 months. Cost is calculated individually for each task. Typical savings from implementing a quality control system can be significant per production line.

We have been in the market for over 5 years and completed 60+ computer vision projects. We will evaluate your project end-to-end—request a consultation to get a quote and technical proposal.