FruitNeRF

:loudspeaker: News

• 26.05.2025: We released :apple: FruitNeRF++ :pear:. Code and Paper available!
• 24.09.2024: Thanks to johnnynunez for employing FruitNeRF on an NVIDIA Jetson
• 12.08.2024: Paper is available on Arxiv.
• 30.06.2024: Paper got accepted to IROS.

Installation

Install Nerfstudio

0. Install Nerfstudio dependencies

Follow these instructions up to and including " tinycudann" to install dependencies and create an environment.

Important: In Section Install nerfstudio please install version 0.3.2 via pip install nerfstudio==0.3.2 not the latest one!

1. Clone this repo

git clone https://github.com/meyerls/FruitNeRF.git

2. Install this repo as a python package

Navigate to this folder and run python -m pip install -e .

3. Run ns-install-cli

Checking the install

Run ns-train -h: you should see a list of "subcommand" with fruit_nerf included among them.

Install Grounding-SAM

Please install Grounding-SAM into the segmentation folder. More details can be found in install segment anything and install GroundingDINO. A copied variant is listed below.

# Start from FruitNerf root folder.
cd segmentation 

# Clone GroundedSAM repository and rename folder
git clone https://github.com/IDEA-Research/Grounded-Segment-Anything.git grounded_sam
cd grounded_sam

# Checkout version compatible with FruitNeRF
git checkout fe24

You should set the environment variable manually as follows if you want to build a local GPU environment for Grounded-SAM:

export AM_I_DOCKER=False
export BUILD_WITH_CUDA=True
export CUDA_HOME=/path/to/cuda-11.3/

Install Segment Anything:

python -m pip install -e segment_anything

Install Grounding DINO:

pip install --no-build-isolation -e GroundingDINO

Install diffusers and misc:

pip install --upgrade diffusers[torch]

pip install opencv-python pycocotools matplotlib onnxruntime onnx ipykernel

Download pretrained weights

cd .. # Download into grounded_sam
wget https://dl.fbaipublicfiles.com/segment_anything/sam_vit_h_4b8939.pth
wget https://github.com/IDEA-Research/GroundingDINO/releases/download/v0.1.0-alpha/groundingdino_swint_ogc.pth

Install SAM-HQ

pip install segment-anything-hq

Download SAM-HQ checkpoint from here (We recommend ViT-H HQ-SAM) into the Grounded-Segment-Anything folder.

Done!

Using FruitNeRF

Now that FruitNeRF is installed you can start counting fruits! You can use your own data, our real or synthetic FruitNeRF Dataset or the Fuji Dataset. If you use ower FruitNeRF dataset you can skip the preparations step and jump to Training.

Prepare own Data

For our data and the Fuji dataset you first have to compute the intrinsic and extrinsic camera parameters and segment the images using grounded-SAM:

ns-process-fruit-data --data {path/to/image-dir} --output-dir {path/to/output-dir} --segmentation-class [Str+Str+Str]

• --data [PATH]: Path the data, either a video file or a directory of images.
• --output-dir [PATH]: Path to the output directory.
• --segmentation-class [Str+Str+Str+...]] Text prompt for segmentation with Grounded SAM. Multiple arguments are also valid.
• --num_downscales [INT]: Number of times to downscale the images. Default is 3.
• --text_threshold [FLOAT] Threshold for text prompt/class to segment images. Default value is 0.15.
• --box_threshold [FLOAT] Threshold for bounding box prediction. Default value is 0.15.
• --data_semantic [PATH]: Predefined path to precomputed masks.
• --skip-colmap: skips COLMAP and generates transforms.json if possible.
• --skip_image_processing: skips copying and downscaling of images and only runs COLMAP if possible and enabled.
• --flag_segmentation_image_debug: saves the masks overlay on rgb images.

If you already have binary segmentation masks please parse the image folder:

ns-prepocess-fruit-data --data {path/to/image-dir} --output-dir {path/to/output-dir} --data_semantic {path/to/seg-dir} 

Training

FruitNerf (~15min)

ns-train fruit_nerf --data {path/to/workspace-dir} --output-dir {path/to/output-dir}

FruitNerf Big (~3h)

ns-train fruit_nerf_big --data {path/to/workspace-dir} --output-dir {path/to/output-dir}

Volumetric Sampling

ns-export-semantics semantic-pointcloud --load-config {path/to/config.yaml} --output-dir {path/to/export/dir} --use-bounding-box True --bounding-box-min -1 - 1 -1 --bounding-box-max 1 1 1 --num_rays_per_batch 2000 --num_points_per_side 2000

Point Cloud Clustering / Fruit Counting

Clustering is not integrated into the nerfstudio pipeline. Therefore, we have created a specific cluster script (clustering\run_clustering.py).

If you want to use it for your own data you have to create a config profile first:

Apple_GT_1024x1024_300 = {
    "path": "/path/2/extracted/pcd/semantic_colormap.ply",
    "remove_outliers_nb_points": 200, # Clean pcd
    "remove_outliers_radius": 0.01, # Clean pcd
    "down_sample": 0.001, # Voxel downsample for faster computation / clustering
    "eps": 0.01,
    "cluster_merge_distance": 0.04, # Merge distance for small clusters
    "minimum_size_factor": 0.3,
    "min_samples": 100, # Min cluster point size
    'template_path': './clustering/apple_template.ply', # Template apple /fruit
    'apple_template_size': 0.7, # Scale apple template if no gt size is available
    'gt_cluster': "/path/2/gt/mesh/fruits.obj", # or None
    "gt_count": 283 # or None
}

Afterward perform the Clustering (see more information in clustering\run_clustering.py!):

Baum = Apple_GT_1024x1024_300
clustering = Clustering(remove_outliers_nb_points=Baum['remove_outliers_nb_points'],
                        remove_outliers_radius=Baum['remove_outliers_radius'],
                        voxel_size_down_sample=Baum['down_sample'],
                        template_path=Baum['template_path'],
                        min_samples=Baum['min_samples'],
                        apple_template_size=Baum['apple_template_size'],
                        gt_cluster=Baum['gt_cluster'],
                        cluster_merge_distance=Baum['cluster_merge_distance'],
                        gt_count=Baum['gt_count']
                        )
count = clustering.count(pcd=Baum["path"], eps=Baum['eps'])

For reproducibility, we provide the extracted point clouds for our synthetic and real-world data. From Table I and Fig.8. Data can be downloaded from here.

Download Data

To reproduce our counting results you can download the extracted point clouds for every training run. Download can be found here: tbd.

Synthetic Dataset

Link:

Real Dataset

Link:

Bibtex

If you find this useful, please cite the paper!