Binvox

Author: i | 2025-04-24

BINVOX File What are BINVOX files and how to open them. Are you having problems opening a BINVOX file or are you simply curious about its contents? We're here to explain the properties of these files and provide you with software that can open or handle your BINVOX files. What is a BINVOX file? A .BINVOX file is a BINVOX Voxel File Format file. BINVOX File What are BINVOX files and how to open them. Are you having problems opening a BINVOX file or are you simply curious about its contents? We're here to explain the properties of these files and provide you with software that can open or handle your BINVOX files. What is a BINVOX file? A .BINVOX file is a BINVOX Voxel File Format file.

binvox/vox/binvox/binvox.cc at master jingma-git/binvox - GitHub

Objfile-to-binvox-to-nparrayThis is a set of tools to help transform a 3D object file like a car or hand into a 3D numpy array by first transforming it into a voxal space (think mind craft) and then import it into python as an numpy array.Obj-to-voxBinvox is a little program toconvert 3D models into binary voxel format. The .binvox file format is asimple run length encoding format described here Binvox( from 3D object to vox space: double-click on the file voxprius-high-res.bat. You'll see the conversion take place, then a view of the model will come up. To get out of the viewer, close it and the controlling window (or hit Control-C in that window).rem delete any previous del toyota-prius.binvoxrem use default resolution of 256binvox toyota-prius.obj( final vox space: double-click on the file view-car.batrem now display itviewvox toyota-prius.binvoxVox-to-npOnce you have exported the binvox model you can now import the file into python with this smallSmall Python module to read and write .binvox files. The voxel data isrepresented as dense 3-dimensional Numpy arrays in Python.Suppose you have a voxelized car model, toyota-prius.binvoxThen:import binvox_rwwith open('toyota-prius.binvox', 'rb') as f: model = binvox_rw.read_as_3d_array(f)You get the idea. model.data has the boolean 3D array. You can thenmanipulate however you wish. For example, here we dilate it withscipy.ndimage and write the dilated version to disk:import scipy.ndimagescipy.ndimage.binary_dilation(model.data.copy(), output=model.data)model.write('dilated.binvox')##Credits and other stuff:advanced: CUDA C mesh voxelizer from DLL from model from model from who knows where.Daniel Maturana for the binvox-rw-py python module:

binvox used to convert VRML to BINVOX format

A python virtual environment and install the necessary dependencies to run the demo.Install Python, pip and virtualenvOn Ubuntu, Python is automatically installed and pip is usually installed. Confirm the python and pip versions: python -V # Should be 2.7.x pip -V # Should be 10.x.xInstall these packages on Ubuntu:sudo apt-get install python-pip python-dev python-virtualenvCreate a virtual environment and install all dependenciescd the_folder_contains_this_READEMEvirtualenv rendernetenvsource rendernetenv/bin/activatepip install -r requirement.txtDownload pre-trained model the pb file and move it into the "model" folder.Helpusage: RenderNet_demo.py [-h] [--voxel_path VOXEL_PATH] [--azimuth AZIMUTH] [--elevation ELEVATION] [--light_azimuth LIGHT_AZIMUTH] [--light_elevation LIGHT_ELEVATION] [--radius RADIUS] [--render_dir RENDER_DIR] [--rotate ROTATE]optional arguments: -h, --help show this help message and exit --voxel_path VOXEL_PATH Path to the input voxel. (default: ./voxel/Misc/bunny.binvox) --azimuth AZIMUTH Value of azimuth, between (0,360) (default: 250) --elevation ELEVATION Value of elevation, between (0,360) (default: 60) --light_azimuth LIGHT_AZIMUTH Value of azimuth for light, between (0,360) (default: 250) --light_elevation LIGHT_ELEVATION Value of elevation for light, between (0,360) (default: 60) --radius RADIUS Value of radius, between (2.5, 4.5) (default: 3.3) --render_dir RENDER_DIR Path to the rendered images. (default: ./render) --rotate ROTATE Flag rotate and render an object by 360 degree in azimuth. Overwrites early settings in azimuth. (default: False)Example: rotate bunny by 360 degreespython RenderNet_demo.py --voxel_path ./voxel/Misc/bunny.binvox --rotateconvert -delay 10 -loop 0 ./render/*.png animation.gifExample: chairpython RenderNet_demo.py --voxel_path ./voxel/Chair/64.binvox \ --azimuth 250 \ --elevation 60 \ --light_azimuth 90 \ --light_elevation 90 \ --radius 3.3 \ --render_dir ./renderExample: rotate an object by 360 degreespython RenderNet_demo.py --voxel_path ./voxel/Chair/64.binvox --rotatepython RenderNet_demo.py --voxel_path ./voxel/Table/0.binvox --rotatepython RenderNet_demo.py --voxel_path ./voxel/Misc/tyra.binvox --rotateUninstallrm -rf

BINVOX File: How to open BINVOX file (and what it is)

. BINVOX File What are BINVOX files and how to open them. Are you having problems opening a BINVOX file or are you simply curious about its contents? We're here to explain the properties of these files and provide you with software that can open or handle your BINVOX files. What is a BINVOX file? A .BINVOX file is a BINVOX Voxel File Format file.

Binvox for Windows - FREE Download Binvox for Windows 1.10

Smart-FluidnetSmart-Fluidnet is a framework that automates model generation for fluid dynamic simulation. It is developed by PASA lab ( at University of California, Merced. Smart-Fluidnet provides flexibility and generalization to automatically search the best neural network(NN) models for different input problems.Step 1: Installing mantaflowThe first step is to download the custom manta fork.git clone git@github.com:kristofe/manta.gitNext, you must build mantaflow using the cmake system.cd FluidNet/mantamkdir buildcd buildsudo apt-get install doxygen libglu1-mesa-dev mesa-common-dev qtdeclarative5-dev qml-module-qtquick-controlscmake .. -DGUI='OFF' make -j8Step 2: Generating input problemsWe use a subset of the NTU 3D Model Database models ( Please download the model files:cd FluidNet/voxelizermkdir objscd objswget wget # Alternate download location.unzip NTU3D.v1_0-999.zipwget we use the binvox library ( to create voxelized representations of the NTU models. Download the executable for your platform and put the binvox executable file in FluidNet/voxelizer. Then run our script:cd FluidNet/voxelizerchmod u+x binvoxpython generate_binvox_files.pyInstall matlabnoise ( to the SAME path that FluidNet is in. i.e. the directory structure should be:/path/to/FluidNet//path/to/matlabnoise/To install matlabnoise (with python bindings):sudo apt-get install python3.5-devsudo apt-get install swiggit clone git@github.com:jonathantompson/matlabnoise.gitcd matlabnoisesh compile_python3.5_unix.shsudo apt-get install python3-matplotlibpython3.5 test_python.pyNow you're ready to generate the training data. Make sure the directory data/datasets/output_current exists.cd FluidNet/manta/build./manta ../scenes/_trainingData.py --dim 2 --addModelGeometry True --addSphereGeometry TrueStep3: Compiling the dependenciesWe assume that Torch7 is installed, otherwise follow the instructions here. We use the standard distro with the cuda SDK for cutorch and cunn and cudnn.After install torch, compile tfluids:sudo apt-get install freeglut3-devsudo apt-get install libxmu-dev libxi-devcd FluidNet/torch/tfluidsluarocks make tfluids-1-00.rockspecNote: some users are reporting that you need to explicitly install

GitHub - andstor/binvox: :page_facing_up: Parser and builder for BINVOX

BodyNet: Volumetric Inference of 3D Human Body ShapesGül Varol, Duygu Ceylan, Bryan Russell, Jimei Yang, Ersin Yumer, Ivan Laptev and Cordelia Schmid,BodyNet: Volumetric Inference of 3D Human Body Shapes, ECCV 2018.[Project page] [arXiv] Contents1. Preparation2. Training3. Testing4. Fitting SMPL modelCitationAcknowledgements1. Preparation1.1. RequirementsDatasetsDownload SURREAL and/or Unite the People (UP) dataset(s)TrainingInstall Torch with cuDNN support.Install matio by luarocks install matioInstall OpenCV-Torch by luarocks install cvTested on Linux with cuda v8 and cudNN v5.1.Pre-processing and fitting python scriptsPython 2 environment with the following installed:OpenDrChumpyOpenCVSMPL relatedDownload SMPL for python and set SMPL_PATHFix the naming: mv basicmodel_m_lbs_10_207_0_v1.0.0 basicModel_m_lbs_10_207_0_v1.0.0Do the following changes in the code smpl_webuser/verts.py:- v_template, J, weights, kintree_table, bs_style, f,+ v_template, J_regressor, weights, kintree_table, bs_style, f,- if sp.issparse(J):- regressor = J- J_tmpx = MatVecMult(regressor, v_shaped[:,0])- J_tmpy = MatVecMult(regressor, v_shaped[:,1])- J_tmpz = MatVecMult(regressor, v_shaped[:,2])+ if sp.issparse(J_regressor):+ J_tmpx = MatVecMult(J_regressor, v_shaped[:,0])+ J_tmpy = MatVecMult(J_regressor, v_shaped[:,1])+ J_tmpz = MatVecMult(J_regressor, v_shaped[:,2])+ assert(ischumpy(J_regressor))- assert(ischumpy(J))+ result.J_regressor = J_regressorDownload neutral SMPL model and place under models folder of SMPLDownload SMPLify and set SMPLIFY_PATHVoxelization relatedDownload binvox executable and set BINVOX_PATHDownload binvox python package and set BINVOX_PYTHON_PATH1.2. Pre-processing for trainingSURREAL voxelizationLoop over the dataset and run preprocess_surreal_voxelize.py for each _info.mat file by setting it with the --input option (for foreground and/or part voxels with the --parts option). The surface voxels are filled with imfill with the preprocess_surreal_fillvoxels.m script, but you could do it in python (e.g. ndimage.binary_fill_holes(binvoxModel.data)). Sample preprocessed data is included in preprocessing/sample_data/surreal.Preparing UP dataLoop over the dataset by running preprocess_up_voxelize.py to voxelize and to re-organize the dataset. Fill the voxels with preprocess_up_fillvoxels.m. Preprocess the segmentation maps with preprocess_up_segm.m. Sample preprocessed data is included in preprocessing/sample_data/up.1.3. Setup paths for trainingPlace the data under ~/datasets/SURREAL and ~/datasets/UP or change the opt.dataRoot in opts.lua. The outputs will be written to ~/cnn_saves//, you can change the opt.logRoot to change the cnn_saves location.1.4. Download pre-trained modelsWe provide several pre-trained models used in the paper bodynet.tar.gz (980MB). The content is explained in the training section. Extract the .t7 files and place them under models/t7 directory.# Trained on SURREALmodel_segm_cmu.t7model_joints3D_cmu.t7model_voxels_cmu.t7model_voxels_FVSV_cmu.t7model_partvoxels_FVSV_cmu.t7model_bodynet_cmu.t7# Trained on UPmodel_segm_UP.t7model_joints3D_UP.t7model_voxels_FVSV_UP.t7model_voxels_FVSV_UP_manualsegm.t7model_bodynet_UP.t7# Trained on MPIImodel_joints2D.t72. TrainingThere are sample scripts under training/exp/backup directory. These were created automatically using the training/exp/run.sh script. For example the following run.sh script:source create_exp.sh -hinput="rgb"supervision="segm15joints2Djoints3Dvoxels" inputtype="gt"extra_args="_FVSV"running_mode="train"#modelno=1dataset="cmu"create_cmdcmd="${return_str} \\-batchSize 4 \\-modelVoxels models/t7/model_voxels_FVSV_cmu.t7 \\-proj silhFVSV \"run_cmdgenerates and runs the following script:cd ..qlua main.lua \-dirName segm15joints2Djoints3Dvoxels/rgb/gt_FVSV \-input rgb \-supervision segm15joints2Djoints3Dvoxels \-datasetname cmu \-batchSize 4 \-modelVoxels models/t7/model_voxels_FVSV_cmu.t7 \-proj silhFVSV \This trains the final version of the model described in the paper, i.e., training end-to-end network with pre-trained subnetworks with multi-task losses and multi-view re-projection losses. If you manage to run this on the SURREAL dataset, the standard output should resemble the following:Epoch: [1][1/2000] Time: 66.197, Err: 0.170 PCK: 87.50, PixelAcc: 68.36, IOU: 55.03, RMSE: 0.00, PE3Dvol: 33.39, IOUvox: 66.56, IOUprojFV: 92.89, IOUprojSV: 75.56, IOUpartvox: 0.00, LR: 1e-03, DataLoadingTime 192.286Epoch: [1][2/2000] Time: 1.240, Err: 0.472 PCK: 87.50, PixelAcc: 21.38, IOU: 18.79, RMSE: 0.00, PE3Dvol: 44.63, IOUvox: 44.89, IOUprojFV: 73.05, IOUprojSV: 65.19, IOUpartvox: 0.00, LR: 1e-03, DataLoadingTime 0.237Epoch: [1][3/2000] Time: 1.040, Err: 0.318 PCK: 65.00, PixelAcc: 49.58, IOU: 35.99, RMSE: 0.00, PE3Dvol:

mikolalysenko/mesh-to-binvox: Converts a mesh to a binvox stream - GitHub

binvox-rw-py/binvox_rw.py at public dimatura/binvox-rw-py

Code release for RenderNet: A deep convolutional network for differentiable rendering from 3D shapesAll these objects are rendered with the same networkRenderNet: A deep convolutional network for differentiable rendering from 3D shapesThu Nguyen-Phuoc, Chuan Li, Stephen Balaban, Yong-liang Yang(To appear) Neural Information Processing Systems 2018DatasetIf you want to use your own data, the training images must be combined into a *.tar file, and the voxel files can be stored in a directory. To create TAR file:python tools/create_TAR.py --images_path --save_path --file_format --to_compressDownload the mesh voxeliser here: with Ubuntu 16.04, Tensorflow 1.4, CUDA 8.0, cuDNN 6.Download the datasets and put them in the " data" folderTo run the training of rendering shaderspython render/RenderNet_Shader.py config_RenderNet.jsonHelp with configimage_path: path to training images Tar file for trainingimage_path_valid: path to training images Tar file for validationmodel_path: path to binvox models diretoryis_greyscale: "True" if the training images are greyscale, "False" otherwisegpu: number of the GPU to use. Default: 0batch_size: size of training batches. Default: 24max_epochs: number of epochs to train. Default: 20threshold: threshold to binarized voxel grids. Default: 0.1e_eta: learning rate. Default: 0.00001keep_prob: the probability that each element is kept, used for Dropout. Default: 0.75decay_steps: number of udpates before learning rate decayse.trained_model_name: named of the trained model. Default: "RenderNet"sample_save: path to save the training resultscheck_point_secs: Timelapse before saving the trained model. Default: 7200To run the training of rendering texturepython render/RenderNet_Textue_Face_Normal.py config_RenderNet_texture.jsonHelp with configimage_path: path to training images Tar file for trainingimage_path_valid: path to training images Tar file for validationnormal_path: path to normal maps diretorytexture_path: path to texture. BINVOX File What are BINVOX files and how to open them. Are you having problems opening a BINVOX file or are you simply curious about its contents? We're here to explain the properties of these files and provide you with software that can open or handle your BINVOX files. What is a BINVOX file? A .BINVOX file is a BINVOX Voxel File Format file. BINVOX File What are BINVOX files and how to open them. Are you having problems opening a BINVOX file or are you simply curious about its contents? We're here to explain the properties of these files and provide you with software that can open or handle your BINVOX files. What is a BINVOX file? A .BINVOX file is a BINVOX Voxel File Format file.

binvox-rw-py/README.md at public dimatura/binvox-rw-py

Diretorymodel_path: path to binvox models diretorygpu: number of the GPU to use. Default: 0batch_size: size of training batches. Default: 24max_epochs: number of epochs to train. Default: 20threshold: threshold to binarized voxel grids. Default: 0.1e_eta: learning rate. Default: 0.00001keep_prob: the probability that each element is kept, used for Dropout. Default: 0.75decay_steps: number of udpates before learning rate decays. Defaule: 90000trained_model_name: named of the trained model. Default: "RenderNet"sample_save: path to save the training resultscheck_point_secs: Timelapse before saving the trained model. Default: 7200To run the reconstruction from imagepython reconstruction/Reconstruct_RenderNet_Face.py config_.jsonHelp with configtarget_normal: path to the target normal map. Used to create the final shaded image for inverse-renderingtarget_albedo: path to the target albedo. Used to create the final shaded image for inverse-renderingweight_dir: path to the weights of a pretrained RenderNetweight_dir_decoder: path to the weights of a pretrained shape autoencodergpu: number of the GPU to use. Default: 0batch_size: size of training batches. Default: 24max_epochs: number of epochs to train. Default: 20z_dim: dimension of the shape latent vector. Default: 200threshold: threshold to binarized voxel grids. Default: 0.3shape_eta: Learning rate to update the reconstructed shape vector. Default: 0.8pose_eta: Learning rate to update the reconstructed pose.Default:0.01tex_eta: Learning rate to update the reconstructed texture vector. Default: 0.8light_eta: Learning rate to update the reconstructed light. Default: 0.4decay_steps: Default: 90000trained_model_name: named of the trained model. Default: "RenderNet"sample_save: path to save the training resultscheck_point_secs: Timelapse before saving the trained model. Default: 3600Demo of a trained RenderNet for Phong shadingTested with Ubuntu 16.04, Tensorflow 1.8, CUDA 9.0, cuDNN 7.The following steps set up

Converting .step file to binvox Issue 8 dimatura/binvox-rw-py

. BINVOX File What are BINVOX files and how to open them. Are you having problems opening a BINVOX file or are you simply curious about its contents? We're here to explain the properties of these files and provide you with software that can open or handle your BINVOX files. What is a BINVOX file? A .BINVOX file is a BINVOX Voxel File Format file.

binvox 1.10 - Download - Softpedia