NeuGrasp: Generalizable Neural Surface Reconstruction with Background
  Priors for Material-Agnostic Object Grasp Detection
Authors: name: Qingyu Fan, name: Yinghao Cai, name: Chao Li, name: Wenzhe He, name: Xudong Zheng, name: Tao Lu, name: Bin Liang, name: Shuo Wang

Robotic grasping in scenes with transparent and specular objects presents
great challenges for methods relying on accurate depth information. In this
paper, we introduce NeuGrasp, a neural surface reconstruction method that
leverages background priors for material-agnostic grasp detection. NeuGrasp
integrates transformers and global prior volumes to aggregate multi-view
features with spatial encoding, enabling robust surface reconstruction in
narrow and sparse viewing conditions. By focusing on foreground objects through
residual feature enhancement and refining spatial perception with an
occupancy-prior volume, NeuGrasp excels in handling objects with transparent
and specular surfaces. Extensive experiments in both simulated and real-world
scenarios show that NeuGrasp outperforms state-of-the-art methods in grasping
while maintaining comparable reconstruction quality. More details are available
at https://neugrasp.github.io/.