nvs2colmap 0.1.0

Utilities for converting novel view synthesis datasets to COLMAP format.

NVS2COLMAP

Utilities for converting novel view synthesis datasets to COLMAP format.

Supported Formats

• Neural 3D Video Dataset: scenes with poses_bounds.npy and one mp4 file per camera. See nvs2colmap/n3dv/README.md.

Supported Datasets

• Neural 3D Video Dataset: dataset facebookresearch/Neural_3D_Video, paper Neural 3D Video Synthesis from Multi-view Video.
• StreamRF / Meet Room Dataset: dataset AlgoHunt/StreamRF, paper Streaming Radiance Fields for 3D Video Synthesis.
• Robo360: dataset liuyubian/Robo360, paper Robo360: A 3D Omnispective Multi-Material Robotic Manipulation Dataset.

Quick Start

Install the Python runtime dependencies:

pip install numpy torch

For Neural 3D Video scenes, the command also needs ffmpeg and ffprobe on PATH, or explicit paths via --ffmpeg and --ffprobe. If you want to run the full COLMAP pipeline, also provide a COLMAP executable via --colmap-executable.

Extract a Neural 3D Video scene and write per-frame COLMAP text models:

python -m nvs2colmap.n3dv \
  --path data/coffee_martini \
  --ffmpeg ffmpeg \
  --ffprobe ffprobe \
  --n-frames 300

Run the full COLMAP pipeline for each frame:

python -m nvs2colmap.n3dv \
  --path data/Robo360/xarm6_gold_rope_in_basket_2 \
  --ffmpeg D:/MyPrograms/ffmpeg.exe \
  --ffprobe D:/MyPrograms/ffprobe.exe \
  --video-extension MP4 \
  --n-frames 1 \
  --use-colmap \
  --colmap-executable data/colmap/COLMAP.bat \
  --colmap-use-gpu 1

By default, decoded frames are written to frame*/images, and the command also writes frame*/sparse/0 text models. With --use-colmap, decoded frames are written to frame*/input, and each frame additionally gets the standard COLMAP outputs such as distorted/, images/, sparse/, and stereo/.