📃Scientific paper: Planner3D: LLM-enhanced graph prior meets 3D indoor scene explicit
regularization
Abstract:
Compositional 3D scene synthesis has diverse applications across a spectrum
of industries such as robotics, films, and video games, as it closely mirrors
the complexity of real-world multi-object environments.
Conventional works
typically employ shape retrieval based frameworks which naturally suffer from
limited shape diversity.
Recent progresses have been made in object shape
generation with generative models such as diffusion models, which increases the
shape fidelity.
However, these approaches separately treat 3D shape generation
and layout generation.
The synthesized scenes are usually hampered by layout
collision, which suggests that the scene-level fidelity is still
under-explored.
In this paper, we aim at generating realistic and reasonable 3D
indoor scenes from scene graph.
To enrich the priors of the given scene graph
inputs, large language model is utilized to aggregate the global-wise features
with local node-wise and edge-wise features.
With a unified graph encoder,
graph features are extracted to guide joint layout-shape generation.
Additional
regularization is introduced to explicitly constrain the produced 3D layouts.
Benchmarked on the SG-FRONT dataset, our method achieves better 3D scene
synthesis, especially in terms of scene-level fidelity.
The source code will be
released after publication.
;Comment: 16 pages, 10 figures
Continued on ES/IODE ➡️ https://etcse.fr/k9G
-------
If you find this interesting, feel free to follow, comment and share.
We need your help to enhance our visibility, so that our platform continues to serve you.
Learn more: https://eu1.hubs.ly/H0bL8gL0