Artec Studio vs Nerfstudio
Artec Studio
artec3d.comNerfstudio
nerf.studioSide by side
| Figure | Artec Studio | Nerfstudio |
|---|---|---|
| Price from | $40/mo | — |
| Free plan | — | — |
| Free trial | Yes | — |
| Open source | — | — |
| Web | — | Yes |
| Windows | Yes | Yes |
| Linux | — | Yes |
| API | — | Yes |
| Self-hosted | — | Yes |
| Plans published | 7 | 0 |
| Spec score · AI 3D Scanning Software | 8.1 (#9) | 7.5 (#19) |
| Purpose | Artec Studio is professional 3D scanning and photogrammetry software for 3D modeling, reverse engineering, and inspection. | Nerfstudio provides a modular API for creating, training, and testing Neural Radiance Fields (NeRFs). |
| Support | Artec advertises 24/7 online support for Artec Studio users. | The project directs users with questions to its Discord and provides documentation, tutorials, and a feedback form. |
Plans
Artec Studio
- Artec Studio Pro: TrialFree
- 1-month Artec Studio Lite Individual$48 a month
- 1-month Artec Studio Lite Business$96 a month
- 1-year Artec Studio Lite Individual$480 a year
- 1-year Artec Studio Lite Business$960 a year
- Artec Studio Pro: Subscription$1,700 a year
- Artec Studio Pro: Lifetime$4,300, paid once
In their own words
Artec Studio
Artec Studio has no write-up on Specifiction yet; its figures are above.
Nerfstudio
Nerfstudio is a free, open-source API and toolkit for creating, training, and testing Neural Radiance Fields through modular components. Included methods are Nerfacto, Instant-NGP, NeRF, Mip-NeRF, TensoRF, and Splatfacto, its Gaussian Splatting implementation. Command-line tools process data, train and evaluate models, render videos, view trained scenes, and export models. A web viewer shows training in real time, lets users interact with scenes, renders custom camera trajectories, and creates videos. Share URLs allow others to watch progress, but those links are publicly accessible and can add latency. Documented capture workflows use COLMAP, Polycam, or Record3D to turn phone video into a 3D render. Installation is available through pip, source, or Docker. Docker requires NVIDIA GPU drivers supporting CUDA 11.8, and the quickstart requires an NVIDIA video card with CUDA installed. The project supports Tensorboard and Wandb logging, profiling, and debugging. It runs across Linux, Windows, web, API, and self-hosted environments, with Windows described as less tested and more fragile.