🚀 Project Guide 🚀

The 3DRepo repository is mainly used to reproduce currently cutting-edge 3D reconstruction technologies, including implicit technologies such as Nerf, MipNerf, Tri-MipNerf, and explicit technical models such as 3D Gaussian Splatting, 2D Gaussian Splatting, and Mip-Splatting. The datasets used in the project mainly include datasets like Nerf-Synthetic and Mip-360 (if you need to download the datasets, you can click Nerf Synthetic and Mip-360 to download them). Additionally, the project also includes processing methods and visualization operations for these data. You can view the performance of these models on real datasets by watching the effects of specific synthetic perspectives or checking specific metrics such as PSNR, SSIM, and LPIPS.

🔧 1.Experiment Results 🔧

We choose the Nerf-Synthetic and Mip-360 dataset to train the 3D Gaussian Splatting, 2D Gassian Splatting, Gaussian Opacity Fields, Mip-Splatting, Analytic-Splatting and 3DGRUT algorithms and make Single-Train-Single-Test(STST), Single-Train-Multiple-Test(STMT) experiments to get metrics about PSNR, SSIM, LPIPS to compare the performance of algorithms. The detailed results of these experiments are as follows:

🔍 2.Colmap Tool 🔍

Colmap first reconstructs the sparse 3D structure and camera poses through SfM, then uses MVS to densify the sparse points, and finally outputs a dense point cloud or mesh. Overall, Colmap's entire pipeline is accelerated by a CPU/GPU hybrid, with high precision, robustness, and open-source availability, making it the "offline reconstruction baseline" in academia and industry. The specific process for installing the GPU version of Colmap on Linux (taking Ubuntu 22.04 as an example) is as follows:

# Install the compilation dependencies
sudo apt update
sudo apt install -y gcc-11 g++-11
git clone https://github.com/colmap/colmap.git
cd colmap

#If you encounter compilation issues with PoseLib, you need to manually download the PoseLib source code package.
cd ~/colmap
wget https://github.com/PoseLib/PoseLib/archive/f119951fca625133112acde48daffa5f20eba451.zip
# Unzip to the diectional floder
unzip -q f119951fca625133112acde48daffa5f20eba451.zip
mv PoseLib-f119951fca625133112acde48daffa5f20eba451 \
   build/_deps/poselib-src

# Rebuild and trigger CMake
cd build
cmake .. -GNinja \
  -DCMAKE_BUILD_TYPE=Release \
  -DCMAKE_CUDA_ARCHITECTURES=86 \
  -DBUILD_TESTING=OFF \
  -DBUILD_EXAMPLES=OFF \
  -DCMAKE_C_COMPILER=gcc-11 \
  -DCMAKE_CXX_COMPILER=g++-11

# Continuing to compilation
ninja -j4
sudo ninja install
colmap patch_match_stereo --help | grep gpu

flowchart TD
    A[COLMAP 三维重建流程] --> B[1. 特征提取\nextract_features]
    A --> C[2. 特征匹配\nexhaustive_matcher / sequential_matcher / vocab_tree_matcher]
    A --> D[3. 稀疏重建\nmapper]
    A --> E[4. 模型转换\nmodel_converter]
    A --> F[5. 稠密重建\nimage_undistorter + patch_match_stereo + stereo_fusion]
    B --> B1["作用：\n- 提取每张图像的 SIFT 特征点\n- 生成特征描述子\n- 输出 .bin 文件"]
    C --> C1["作用：\n- 匹配不同图像间的特征点\n- 构建对应关系（2D-2D）\n- 支持多种策略：\n  • exhaustive（穷举，小数据集）\n  • sequential（序列，视频）\n  • vocab_tree（词袋，大数据集）"]
    D --> D1["作用：\n- 初始化相机位姿与稀疏点云\n- 增量式 SfM（Structure from Motion）\n- 输出稀疏重建结果（cameras, images, points3D）"]
    E --> E1["作用：\n- 将二进制模型转为文本格式（便于查看）\n- 或转换坐标系/格式供其他工具使用"]
    F --> F1["作用：\n- image_undistorter：去畸变并重采样图像\n- patch_match_stereo：多视角立体匹配，生成深度图\n- stereo_fusion：融合深度图，生成稠密点云（.ply）"]

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