@@ -53,11 +53,13 @@ See [nerf_loader.cu](src/nerf_loader.cu) for implementation details and addition
## Preparing new NeRF datasets
To train on self-captured data, one has to process the data into an existing format supported by Instant-NGP. We provide scripts to support two complementary approaches:
To train on self-captured data, one has to process the data into an existing format supported by Instant-NGP. We provide scripts to support three approaches:
-[COLMAP](#COLMAP) to create a dataset from a set of photos or a video you took
-[Record3D](#Record3D) to create a dataset with an iPhone 12 Pro or newer (based on ARKit)
-[NeRFCapture](https://github.com/jc211/NeRFCapture) to create a dataset or stream posed images directly to InsantNGP with an iOS device.
Both require [Python](https://www.python.org/) 3.7 or higher to be installed and available in your PATH.
If you are using Debian based Linux distribution, install Python with
...
...
@@ -129,6 +131,9 @@ With an >=iPhone 12 Pro, one can use [Record3D](https://record3d.app/) to collec
```
instant-ngp$ ./instant-ngp path/to/data
```
### NeRFCapture
[NeRFCapture](https://github.com/jc211/NeRFCapture) is an iOS app that runs on any ARKit device. It allows you to stream images directly from your phone to InstantNGP thus enabling a more interactive experience. It can also collect an offline dataset for later use.