Abstract
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 this https URL
Abstract (translated)
合成的3D场景对于开发物理人工智能和生成模型至关重要。现有的程序化生成方法往往具有较低的输出吞吐量，成为扩大数据集创建规模的一个重要瓶颈。在这项工作中，我们介绍了Sceniris，这是一个高效的程序化场景生成框架，可以快速生成大规模且无碰撞的场景变化。此外，Sceniris还提供了一个可选的机器人可达性检查功能，为机器人的任务提供了操作可行性的场景。 Sceniris通过解决先前方法Scene Synthesizer的主要性能限制而设计成了效率最高的状态。它利用批处理采样和cuRobo中的更快碰撞检测来实现至少比Scene Synthesizer快234倍的速度提升。此外，Sceniris还将之前工作中可用的对象级空间关系扩展到支持多样化的场景需求。 我们的代码可以在以下链接中获取：[此URL]（请将"[此URL]"替换为实际提供的GitHub或其它代码托管平台的URL）。
URL
https://arxiv.org/abs/2512.16896
