ScanNet++ Toolkit

Table of Contents

• ScanNet++ Toolkit
  • Requirements
  • DSLR
    • Undistortion: convert fisheye images to pinhole with OpenCV
    • Downscale the DSLR images
    • Undistortion: convert fisheye images to pinhole with COLMAP
    • Render Depth for DSLR and iPhone
  • iPhone
    • Extract RGB frames, masks and depth frames
  • Semantics
    • Prepare 3D Semantics Training Data
    • Split PTH files into chunks for training
    • Visualize training data
    • Prepare Semantic/Instance Ground Truth Files for Evaluation
    • 3D Semantic Segmentation Evaluation
    • 3D Instance Segmentation Evaluation
    • Rasterize 3D Semantics onto 2D Images
  • Novel View Synthesis
    • Novel View Synthesis Evaluation (DSLR)

Dataset Documentation

Please refer to the official dataset documentation which describes the files in the dataset.

The recommended way of accessing individual files and directories is through the scene class.

For evaluation and submission, refer to the submission instructions.

Requirements

conda create -n scannetpp python=3.10
conda activate scannetpp
pip install -r requirements.txt

DSLR

Undistortion: convert fisheye images to pinhole with OpenCV

This is the official undistortion script that generates the undistortion ground-truth in the benchmark. This will generate the undistorted images, mask, and the respective transforms.json file for NeRF training. This is particularly useful if your method support only images with pinhole camera model (e.g., Gaussian Splatting).

Insert data_root in dslr/configs/undistort.yml and run:

python -m dslr.undistort dslr/configs/undistort.yml

Additionally, the user can specify the input and output path in the config files.

Downscale the DSLR images

If you need to downscale the DSLR images to reduce the memory overhead during NeRF training, you can run the following script. The configuration is similar to the undistortion script.

python -m dslr.downscale dslr/configs/downscale.yml

(Deprecated) Undistortion: convert fisheye images to pinhole with COLMAP

User could also use COLMAP to undistort DSLR images (and masks) based on COLMAP so that the output images are pinhole camera models. However, the result here is different from the ones generated by OpenCV.

You will need COLMAP installed to run this script.

Insert data_root and output_dir in dslr/configs/undistort_colmap.yml and run:

python -m dslr.undistort_colmap dslr/configs/undistort_colmap.yml

The output will be saved in output_dir with the following structure:

output_dir/SCENE_ID
├── colmap
│   ├── cameras.txt
│   ├── images.txt
│   └── points3D.txt
├── images
├── masks
└── nerfstudio/transforms.json

Render Depth for DSLR and iPhone

Install the python package from https://github.com/liu115/renderpy in addtion to the requirements.

python -m common.render common/configs/render.yml

The output will be saved in output_dir with the following structure:

output_dir/SCENE_ID/[dslr, iphone]
├── render_rgb
└── render_depth

The rendered depth maps are single-channel uint16 png, where the unit is mm and 0 means invalid depth.

iPhone

Extract RGB frames, masks and depth frames

python -m iphone.prepare_iphone_data iphone/configs/prepare_iphone_data.yml

Semantics

Prepare 3D Semantics Training Data

The meshes may not have a uniform distribution of mesh vertices and voxelizing these could lead to holes in the data. Hence, the vertices must not be treated as a point cloud.

Instead, please sample points on the surface of the mesh and use these as inputs for voxelization, etc.

An example of how to do this is given. This script samples points on the mesh and maps 1.5k+ raw labels to the benchmark classes. The mapping file is at metadata/semantic_benchmark/map_benchmark.csv

Configure the paths in semantic/configs/prepare_training_data.yml

Then run

python -m semantic.prep.prepare_training_data semantic/configs/prepare_training_data.yml

The resulting PTH files are dictionaries with the following keys:

• scene_id - str:
  scene name
• sampled_coords - np(n_samples, 3)(float64):
  coordinates of points sampled on the mesh
• sampled_colors - np(n_samples, 3)(float64):
  rgb colors of points in range [0, 1] (open3d format)
• sampled_num_labels - np(n_samples,)(int16):
  num labels used for the point (only relevant for multilabel)
• sampled_labels - np(n_samples,)(int16):
  semantic labels of points (consecutive starting at 0: corresponds to index in specified semantic label file)
• sampled_instance_labels - np(n_samples,)(int16):
  unique id of instances in scene (non-consecutive because instances might be removed if their semantic class is not contained in the instance label file)
• sampled_instance_anno_id - np(n_samples,)(int16):
  instance ids corresponding to segments_anno.json['segGroups']['id']

Split PTH files into chunks for training

Split the PTH files into smaller chunks of fixed size. For training, use overlapping chunks and for validation, set overlap to 0.

python -m semantic.prep.split_pth_data semantic/configs/split_pth_data_train.yml

Visualize training data

Configure the PTH data dir, scene list and required outputs in semantic/configs/viz_pth_data.yml

python -m semantic.viz.viz_pth_data semantic/configs/viz_pth_data.yml

Prepare Semantic/Instance Ground Truth Files for Evaluation

Prepare PTH files similar to the training data step, but without point sampling. Then configure the PTH data dir, scene list and required outputs in semantic/configs/prepare_semantic_gt.yml and run

python -m semantic.prep.prepare_semantic_gt semantic/configs/prepare_semantic_gt.yml

3D Semantic Segmentation Evaluation

For this you need to prepare the semantic ground truth and predictions in the following format

• one file per scene named <scene_id>.txt, where each line contains the label(s) for the corresponding vertex in the mesh. You can specify either a single label or multiple comma-separate labels in each line. Each line should have the same number of labels, i.e each file should be an N x 1 or N x 3 array for 1 and 3 predictions respectively.

Configure the paths to GT, predictions, label list and downloaded data in semantic/configs/eval_semantic.yml

Then run

python -m semantic.eval.eval_semantic semantic/configs/eval_semantic.yml

3D Instance Segmentation Evaluation

See semantic/eval/eval_instance.py for details on the input formats.

Configure the paths to GT, predictions, label list and downloaded data in semantic/configs/eval_instance.yml

Then run

python -m semantic.eval.eval_instance semantic/configs/eval_instance.yml

Rasterize 3D Semantics onto 2D Images

To get 3D semantics onto 2D DSLR images, run

python -m semantic.prep.rasterize_semantics_2d semantic/configs/rasterize_semantics_2d.yml

Novel View Synthesis

Novel View Synthesis Evaluation (DSLR)

The evaluation script here is the same that runs on the benchmark server. Therefore, it's highly encouraged to run the evaluation script before submitting the results (on the val set) to the benchmark server.

python -m eval.nvs --data_root DATA_ROOT --split SPLIT_FILE --pred_dir PRED_DIR

The PRED_DIR should have the following structure:

SCENE_ID0/
├── DSC00001.JPG
├── DSC00002.JPG
├── ...
SCENE_ID1/
├── ...

NOTE: The evaluation script here is the same that runs on the benchmark server. Therefore, it's highly encouraged to run the evaluation script before submitting the results (on the val set) to the benchmark server.