Dexterous grasping in cluttered environments presents substantial challenges due to the high degrees of freedom of dexterous hands, occlusion, and potential collisions arising from diverse object geometries and complex layouts. To address these challenges, we propose CADGrasp, a two-stage algorithm for general dexterous grasping using single-view point cloud inputs. In the first stage, we predict sparse IBS, a scene-decoupled, contact- and collision-aware representation, as the optimization target. Sparse IBS compactly encodes the geometric and contact relationships between the dexterous hand and the scene, enabling stable and collision-free dexterous grasp pose optimization. To enhance the prediction of this high-dimensional representation, we introduce an occupancy-diffusion model with voxel-level conditional guidance and force closure score filtering. In the second stage, we develop several energy functions and ranking strategies for optimization based on sparse IBS to generate high-quality dexterous grasp poses. Extensive experiments in both simulated and real-world settings validate the effectiveness of our approach, demonstrating its capability to mitigate collisions while maintaining a high grasp success rate across diverse objects and complex scenes.

翻译：在杂乱环境中进行灵巧抓取面临着巨大挑战，这源于灵巧手的高自由度、遮挡以及多样物体几何形状和复杂布局带来的潜在碰撞。为解决这些挑战，我们提出了CADGrasp，一种使用单视角点云输入的通用灵巧抓取两阶段算法。在第一阶段，我们预测稀疏IBS——一种场景解耦、接触与碰撞感知的表征——作为优化目标。稀疏IBS紧凑地编码了灵巧手与场景之间的几何和接触关系，从而能够进行稳定且无碰撞的灵巧抓取姿态优化。为提升这种高维表征的预测效果，我们引入了一种具有体素级条件引导和力闭合分数过滤的占用扩散模型。在第二阶段，我们基于稀疏IBS开发了若干能量函数和排序策略进行优化，以生成高质量的灵巧抓取姿态。在仿真和真实环境中的大量实验验证了我们方法的有效性，证明了其能够在保持高抓取成功率的同时，在不同物体和复杂场景中有效减少碰撞。