Sceniris: A Fast Procedural Scene Generation Framework
This work addresses a bottleneck in scaling synthetic dataset creation for Physical AI and generative models, though it is incremental as it builds on prior methods like Scene Synthesizer.
The paper tackles the problem of low throughput in procedural 3D scene generation, which bottlenecks dataset creation, by introducing Sceniris, a framework that achieves at least 234x speed-up over prior methods while generating collision-free scenes.
Synthetic 3D scenes are essential for developing Physical AI and generative models. Existing procedural generation methods often have low output throughput, creating a significant bottleneck in scaling up dataset creation. In this work, we introduce Sceniris, a highly efficient procedural scene generation framework for rapidly generating large-scale, collision-free scene variations. Sceniris also provides an optional robot reachability check, providing manipulation-feasible scenes for robot tasks. Sceniris is designed for maximum efficiency by addressing the primary performance limitations of the prior method, Scene Synthesizer. Leveraging batch sampling and faster collision checking in cuRobo, Sceniris achieves at least 234x speed-up over Scene Synthesizer. Sceniris also expands the object-wise spatial relationships available in prior work to support diverse scene requirements. Our code is available at https://github.com/rai-inst/sceniris