• https://github.com/megvii-research/introduction-neural-3d-reconstruction

pip install -r requirements.txt

Tested on Ubuntu with torch 1.10 & CUDA 11.3 on TITAN RTX & python 3.7.11

Currently, --ff only supports GPUs with CUDA architecture >= 70.

• 接入数据集，当前仅支持kitti_odometry数据集，首先在当前目录下新建datasets文件夹:

  • kitti_odometry数据可以通过链接获得。下载数据，解压后将整个文件夹拷贝到datasets目录下
  • 如果需要其他场景的数据，请将数据集按照kitti_odometry数据集的格式拷贝到datasets文件夹下，并且修改对应的config
  • 提示：所有数据都放进去直接训练很可能会OOM，作业要求是对场景分块，想要快速测试网络，可以将./nerf/Atlantic_datasets/selector.py里面选择的图片数量改少一点（不能过于少，因为数据处理里面有对于整个场景的归一化操作，过于少无法选择合适的框，建议选择50张图片）
  • 其他：数据集包含pose和image。pose中前面部分和image有序对应。多余的pose可以用做测试渲染新视角的图片（PS：如果是训练场景外的pose，渲染效果差于场景内），也可以选择不用这部分pose，自定义渲染视角测试效果。

• 训练脚本改为通过config配置

  • 如果不指定config文件则会按照默认的配置执行
  • config文件按照：基础配置，数据，网络参数等分级配置
  • 可以通过修改config配置，执行不同的实验

• exercise 相关：

  • 实现“大场景”（包含数据集1600+图片内容的场景）分块融合
  • 本代码只提供训练数据集的前100张图片，仅作为codebase，分块和融合策略请自行添加
  • 提交代码压缩包和可视化效果（图片视频）
    • 可以额外附加文档说明分块融合的策略（也可以写在代码注释中）
    • 可视化不仅要有训练集中的pose渲染图片，还要有val集的pose渲染效果，最好还有其他视角（插值/自定义）的渲染效果（酌情加分）

python ./train_nerf.py -c configs/kitti/kitti_00.yaml

python ./test_nerf.py -c configs/kitti/kitti_00.yaml

若ffmpeg报错，建议通过conda重装ffmpeg

A pytorch implementation of instant-ngp, as described in Instant Neural Graphics Primitives with a Multiresolution Hash Encoding.

• Instead of assuming the scene is bounded in the unit box [0, 1] and centered at (0.5, 0.5, 0.5), this repo assumes the scene is bounded in box [-bound, bound], and centered at (0, 0, 0). Therefore, the functionality of aabb_scale is replaced by bound here.
• For the hashgrid encoder, this repo only implement the linear interpolation mode.
• For the voxel pruning in ray marching kernels, this repo doesn't implement the multi-scale density grid (check the mip keyword), and only use one 128x128x128 grid for simplicity. Instead of updating the grid every 16 steps, we update it every epoch, which may lead to slower first few epochs if using --cuda_ray.
• For the blender dataest, the default mode in instant-ngp is to load all data (train/val/test) for training. Instead, we only use the specified split to train in CMD mode for easy evaluation. However, for GUI mode, we follow instant-ngp and use all data to train (check type='all' for NeRFDataset).

• Credits for the amazing torch-ngp, which provides a pytorch CUDA extension implementation of instant-ngp (sdf and nerf). I am extremely grateful to Ashawkey open source this rewarding code. With this torch version codebase, it is easier to understand instnt-ngp and more convenient to experiment iteration.

• Difference from the original torch-ngp:

  • 保持：
    • 除nerf文件夹以及train_nerf.py, test_nerf.py以外的其他文件和文件夹都保持不变
  • 增加：
    • 场景适配性，以及数据处理的代码
    • depth，seg等监督，以及新增采样策略
  • 修改：
    • 拆分重建了一些原有结构体
    • 优化网络结构

• The framework of NeRF is adapted from nerf_pl:

  @misc{queianchen_nerf,
      author = {Quei-An, Chen},
      title = {Nerf_pl: a pytorch-lightning implementation of NeRF},
      url = {https://github.com/kwea123/nerf_pl/},
      year = {2020},
  }