ACM Transactions on Graphics (Proceedings of SIGGRAPH Asia), December 2024.
Baptiste Nicolet
·
Felix Wechsler
·
Jorge Madrid-Wolff
·
Christophe Moser
·
Wenzel Jakob
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Tomographic Volumetric Additive Manufacturing (TVAM) is an emerging 3D printing technology that can create complex objects in under a minute. The key idea is to project intense light patterns onto a rotating vial of photo-sensitive resin, causing polymerization where the cumulative dose of these patterns reaches the polymerization threshold. We formulate the pattern calculation as an inverse light transport problem and solve it via physically based differentiable rendering. In doing so, we address long-standing limitations of prior work by accurately modeling and correcting for scattering in composite resins, printing in non-symmetric vials, and supporting unusual printing geometries. We also introduce an improved discretization scheme that exploits the ray tracing operation to mitigate resolution-related artifacts in prints. We demonstrate the benefits of our method in real-world experiments, where our computed patterns produce prints with an improved fidelity.
Dr.TVAM is a high-performance inverse rendering framework for tomographic volumetric additive manufacturing. It is based on the Mitsuba renderer, and uses physically-based differentiable rendering to optimize patterns for TVAM. In particular, it supports:
- Printing in scattering media
- Arbitrary vial shapes (round, square, ...)
- Arbitrary projector motions (orthogonal, tilted)
- An improved discretization scheme for the target shape to reduce computational load and to minimize discretization artifacts
For more details we refer to this publication.
- The date is not announced yet, but sign up for the announcement: here
Installing Dr.TVAM can be done via pip:
pip install drtvam
We provide a convenience command-line tool drtvam to run simple optimizations. You can run it as:
drtvam path/to/config.json
Please refer to the documentation for details on the configuration file format. Dr.TVAM will run multi-threaded on your machine but will also use your CUDA GPU and your RT cores if supported by your hardware.
Dr.TVAM provides a set of useful abstractions to implement a wide variety of custom TVAM setups. We show examples in the documentation to get you started.
The full documentation for this project, along with jupyter notebooks explaining the basics of implementing your own optimizations in our framework, can be found on readthedocs.
Can be found on YouTube:
In case you run into issues or you do need support, do not hesitate to open an issue such that we can help you using Dr. TVAM! As an academic user, this is completely free to use. Please reach out to us in case you need support!
This project is provided under a non-commercial license. Please refer to the LICENSE file for details.
When using this project in academic works, please cite the following paper:
@article{nicolet2024inverse,
author = {Nicolet, Baptiste and Wechsler, Felix and Madrid-Wolff, Jorge and Moser, Christophe and Jakob, Wenzel},
title = {Inverse Rendering for Tomographic Volumetric Additive Manufacturing},
journal = {Transactions on Graphics (Proceedings of SIGGRAPH Asia)},
volume = {43},
number={6},
year = {2024},
month = dec,
doi = {10.1145/3687924}
}