快速生成无碰撞3D场景，支持机器人操作可行性检查
Sceniris: A Fast Procedural Scene Generation Framework
- 批量采样结合cuRobo加速碰撞检测，提升生成效率
- 相较Scene Synthesizer提速至少234倍，支持大规模场景生成
- 扩展物体空间关系，适配多样场景需求与机器人任务
合成3D场景对物理人工智能和生成模型的发展至关重要。现有程序化生成方法输出吞吐量低，成为数据集规模化生成的瓶颈。本文提出Sceniris，一个高效程序化场景生成框架，可快速生成大规模、无碰撞的场景变体，并可选添加机器人可达性检查，生成适合机器人操作的任务场景。Sceniris通过解决先前方法Scene Synthesizer的主要性能瓶颈，采用批量采样和cuRobo中的快速碰撞检测，实现至少234倍于Scene Synthesizer的加速。同时，该框架扩展了先前工作中物体间的空间关系，支持更丰富的场景需求。代码已开源：https://github.com/rai-inst/sceniris
原文摘要 · Abstract (English)
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
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