### Setup NeuralRecon Environment

Source: https://context7.com/zju3dv/neuralrecon/llms.txt

Commands to install system dependencies, set up the Conda environment, and download the necessary pretrained model weights.

```bash
sudo apt install libsparsehash-dev
conda env create -f environment.yaml
conda activate neucon
mkdir checkpoints && cd checkpoints
gdown --id 1zKuWqm9weHSm98SZKld1PbEddgLOQkQV
```

--------------------------------

### Start NeuralRecon Training with distributed PyTorch

Source: https://github.com/zju3dv/neuralrecon/blob/master/README.md

This script initiates the distributed training process for NeuralRecon using PyTorch. It sets the visible CUDA devices and launches the main training script with a specified configuration file. Ensure you have PyTorch and its distributed package installed.

```bash
#!/usr/bin/env bash
export CUDA_VISIBLE_DEVICES=0,1
python -m torch.distributed.launch --nproc_per_node=2 main.py --cfg ./config/train.yaml
```

--------------------------------

### Install NeuralRecon Environment

Source: https://github.com/zju3dv/neuralrecon/blob/master/README.md

Commands to install system dependencies and set up the Conda environment for the project.

```shell
sudo apt install libsparsehash-dev
conda env create -f environment.yaml
conda activate neucon
```

--------------------------------

### Initialize and Run NeuralRecon Model

Source: https://context7.com/zju3dv/neuralrecon/llms.txt

Demonstrates how to load the configuration, initialize the NeuralRecon model, load pretrained weights, and perform inference on input data.

```python
import torch
from models import NeuralRecon
from config import cfg, update_config

class Args:
    cfg = './config/demo.yaml'
    opts = []
args = Args()
update_config(cfg, args)

model = NeuralRecon(cfg).cuda().eval()
model = torch.nn.DataParallel(model, device_ids=[0])

state_dict = torch.load('./checkpoints/model_000047.ckpt')
model.load_state_dict(state_dict['model'], strict=False)

with torch.no_grad():
    outputs, loss_dict = model(sample, save_mesh=True)
```

--------------------------------

### Run NeuralRecon Demo

Source: https://context7.com/zju3dv/neuralrecon/llms.txt

Commands to execute the real-time reconstruction demo using custom ARKit data, including configuration for visualization and performance.

```bash
python demo.py --cfg ./config/demo.yaml
```

--------------------------------

### Download Pretrained Model

Source: https://github.com/zju3dv/neuralrecon/blob/master/README.md

Uses gdown to download the pretrained ScanNet weights into the project checkpoints directory.

```bash
mkdir checkpoints && cd checkpoints
gdown --id 1zKuWqm9weHSm98SZKld1PbEddgLOQkQV
```

--------------------------------

### Configure Data Transformation Pipeline

Source: https://context7.com/zju3dv/neuralrecon/llms.txt

Sets up a sequential transformation pipeline for inference, including image resizing, tensor conversion, voxel space mapping, and projection matrix computation.

```python
from datasets import transforms
transform = [
	transforms.ResizeImage((640, 480)),
	transforms.ToTensor(),
	transforms.RandomTransformSpace(voxel_dim=[96, 96, 96], voxel_size=0.04, random_rotation=False, random_translation=False, paddingXY=0, paddingZ=0, max_epoch=50),
	transforms.IntrinsicsPoseToProjection(n_views=9, stride=4)
]
transforms_pipeline = transforms.Compose(transform)
processed_data = transforms_pipeline(data)
```

--------------------------------

### Train NeuralRecon Model

Source: https://context7.com/zju3dv/neuralrecon/llms.txt

Commands to initiate training on ScanNet, supporting both single-GPU and multi-GPU distributed configurations.

```bash
python main.py --cfg ./config/train.yaml
export CUDA_VISIBLE_DEVICES=0,1
python -m torch.distributed.launch --nproc_per_node=2 main.py --cfg ./config/train.yaml
```

--------------------------------

### Initialize GRUFusion Module

Source: https://context7.com/zju3dv/neuralrecon/llms.txt

Initializes the GRU-based fusion module for integrating local TSDF predictions into a global volume.

```python
import torch
from models.gru_fusion import GRUFusion
from config import cfg
gru_fusion = GRUFusion(cfg.MODEL, direct_substitute=True)
```

--------------------------------

### Run Inference and Evaluation

Source: https://github.com/zju3dv/neuralrecon/blob/master/README.md

Commands to execute model inference on the test set and evaluate the resulting meshes.

```bash
python main.py --cfg ./config/test.yaml
python tools/evaluation.py --model ./results/scene_scannet_release_fusion_eval_47 --n_proc 16
python tools/visualize_metrics.py --model ./results/scene_scannet_release_fusion_eval_47
```

--------------------------------

### Train and Infer with NeuralRecon

Source: https://context7.com/zju3dv/neuralrecon/llms.txt

Commands to resume training from a checkpoint and run inference on the ScanNet test set using configuration files.

```bash
python main.py --cfg ./config/train.yaml RESUME True
python main.py --cfg ./config/test.yaml
```

--------------------------------

### Evaluate Reconstructed Meshes

Source: https://context7.com/zju3dv/neuralrecon/llms.txt

Scripts to run depth and mesh metric evaluation, including headless rendering support and metric visualization.

```bash
python tools/evaluation.py --model ./results/scene_scannet_release_fusion_eval_47 --data_path ./data/scannet/scans_test --gt_path /data/scannet/scans_test --n_proc 16 --n_gpu 1
export PYOPENGL_PLATFORM=osmesa
python tools/evaluation.py --model ./results/scene_scannet_release_fusion_eval_47
python tools/visualize_metrics.py --model ./results/scene_scannet_release_fusion_eval_47
```

--------------------------------

### Configure NeuralRecon with YACS (Python)

Source: https://context7.com/zju3dv/neuralrecon/llms.txt

Configures NeuralRecon using YAML files and the YACS configuration system. It loads a base configuration and allows overriding specific options via command-line arguments or a Python list. Configuration values can then be accessed directly from the cfg object.

```python
from config import cfg, update_config

# Load base configuration from YAML file
class Args:
    cfg = './config/train.yaml'
    opts = ['TRAIN.LR', '0.0005', 'BATCH_SIZE', '2']  # Override options
args = Args()
update_config(cfg, args)

# Access configuration values
print(f"Mode: {cfg.MODE}")                    # 'train' or 'test'
print(f"Dataset: {cfg.DATASET}")              # 'scannet' or 'demo'
print(f"Batch size: {cfg.BATCH_SIZE}")        # 1
print(f"Voxel size: {cfg.MODEL.VOXEL_SIZE}")  # 0.04 meters
print(f"Volume dimensions: {cfg.MODEL.N_VOX}")  # [96, 96, 96]
print(f"Number of views: {cfg.TRAIN.N_VIEWS}")  # 9
```

--------------------------------

### Prepare ScanNet Data

Source: https://context7.com/zju3dv/neuralrecon/llms.txt

Scripts for generating ground truth TSDF volumes from raw ScanNet data for both training and testing phases.

```bash
python tools/tsdf_fusion/generate_gt.py --data_path /path/to/scannet --save_name all_tsdf_9 --window_size 9 --voxel_size 0.04 --num_layers 3 --n_proc 16 --n_gpu 2
python tools/tsdf_fusion/generate_gt.py --test --data_path /path/to/scannet --save_name all_tsdf_9 --window_size 9
```

--------------------------------

### Process ARKit Data for NeuralRecon (Python)

Source: https://context7.com/zju3dv/neuralrecon/llms.txt

Processes raw ARKit capture data (video and poses) into the fragment format required by NeuralRecon. It takes the data path and several parameters to control fragment generation, such as window size, minimum angle, and minimum distance between keyframes.

```python
from tools.process_arkit_data import process_data

# Process captured ARKit data
process_data(
    data_path='/path/to/arkit_capture',
    data_source='ARKit',
    window_size=9,           # Number of frames per fragment
    min_angle=15,            # Minimum angle change between keyframes (degrees)
    min_distance=0.1,        # Minimum translation between keyframes (meters)
    ori_size=(1920, 1440),   # Original capture resolution
    size=(640, 480)          # Target resolution
)
```

--------------------------------

### Save and Process TSDF Volumes

Source: https://context7.com/zju3dv/neuralrecon/llms.txt

Utilities for saving reconstructed scenes, handling incremental mesh updates, and converting TSDF volumes to meshes using marching cubes.

```python
from utils import SaveScene
from config import cfg
save_mesh_scene = SaveScene(cfg)
save_mesh_scene.scene_name = 'my_scene'
save_mesh_scene.keyframe_id = 0
if 'scene_tsdf' in outputs:
	save_mesh_scene.save_scene_eval(epoch_idx=47, outputs=outputs)
save_mesh_scene.save_incremental(epoch_idx=47, batch_idx=0, imgs=sample['imgs'][0], outputs=outputs)
import numpy as np
tsdf_volume = outputs['scene_tsdf'][0].cpu().numpy()
origin = outputs['origin'][0].cpu().numpy()
mesh = SaveScene.tsdf2mesh(voxel_size=0.04, origin=origin, tsdf_vol=tsdf_volume)
mesh.export('output_mesh.ply')
```

--------------------------------

### Generate Ground Truth TSDFs

Source: https://github.com/zju3dv/neuralrecon/blob/master/README.md

Parses raw ScanNet data into the processed pickle format and generates ground truth TSDFs using TSDF Fusion.

```bash
python tools/tsdf_fusion/generate_gt.py --data_path PATH_TO_SCANNET --save_name all_tsdf_9 --window_size 9
python tools/tsdf_fusion/generate_gt.py --test --data_path PATH_TO_SCANNET --save_name all_tsdf_9 --window_size 9
```

--------------------------------

### GRU Fusion for Scene Reconstruction (Python)

Source: https://context7.com/zju3dv/neuralrecon/llms.txt

Performs GRU fusion to reconstruct a 3D scene from input data. It takes coordinates, TSDF values, and input dictionaries, with options to control scale and mesh saving. The function can be reset for new scenes.

```python
from neuralrecon.gru_fusion import gru_fusion

# inputs: dict with 'fragment', 'scene', 'vol_origin', 'vol_origin_partial', etc.
outputs = gru_fusion(
    coords=voxel_coords,
    values_in=tsdf_values,
    inputs=inputs,
    scale=2,              # Finest scale (0=coarse, 2=fine)
    outputs=None,
    save_mesh=True
)

# Output contains fused scene reconstruction:
# outputs['scene_tsdf']: list of TSDF volumes
# outputs['origin']: list of volume origins
# outputs['scene_name']: list of scene identifiers

# Reset global volume for new scene
gru_fusion.reset(scale=2)
```

=== COMPLETE CONTENT === This response contains all available snippets from this library. No additional content exists. Do not make further requests.