Inter3D

Inter3D: A Benchmark and Strong Baseline for Human-Interactive 3D Object Reconstruction

The implementation of our code is referenced in kwea123-npg_pl。The hardware and software basis on which our model operates is described next

• Ubuntu 18.04
• NVIDIA GeForce RTX 3090 ,CUDA 11.3

Setup

Let's complete the basic setup before we run the model。

• Clone this repo by

git clone https://github.com/Inter3D-ui/Inter3D.git

• Create an anaconda environment

conda create -n Inter3D python=3.7

• cuda code compilation dependency.

  • Install pytorch by

   conda install pytorch==1.11.0 torchvision==0.12.0 torchaudio==0.11.0 cudatoolkit=11.3 -c pytorch

  • Install torch-scatter following their instruction like

   pip install torch-scatter==2.0.9 -f https://data.pyg.org/whl/torch-1.11.0+cu113.html

  • Install tinycudann following their instrucion(pytorch extension) like

   pip install git+https://github.com/NVlabs/tiny-cuda-nn/#subdirectory=bindings/torch

  • Install apex following their instruction like

   git clone https://github.com/NVIDIA/apex 
   cd apex 
   pip install -v --disable-pip-version-check --no-cache-dir --no-build-isolation --config-settings "--build-option=--cpp_ext" --config-settings "--build-option=--cuda_ext" ./

  • Install core requirements by

   pip install -r requirements.tx

• Cuda extension:please run this each time you pull the code.``.

   pip install models/csrc/
   # (Upgrade pip to >= 22.1)

Datasets

链接: https://pan.baidu.com/s/1Jor9Ke1hzgm5SeNzaiJSnQ?pwd=36ed 提取码: 36ed

Training

python run.py
--root_dir /data/CG/data/car/
--exp_name car_np
--split train
--scale 1
--num_epochs 15
--downsample 0.5
--stage_end_epoch 2
--stage_num 3
--l1TimePlanes_weight 1e-4
--timeSmoothness_weight 1e-3
--distortion_weight 1e-3
--opacity_weight 1e-3
--density_weight 1e-2

result