title: NeuGrasp: Generalizable Neural Surface Reconstruction with Background
    Priors for Material-Agnostic Object Grasp Detection
summary: 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/.
paper_authors._id: 67cd7ace999766d8cd73fb18
paper_authors.name: Qingyu Fan
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-10T08:01:41.233Z
paper_authors._id: 6732f2c24c2f18a60e76b915
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paper_authors.fullname: Fan
paper_authors.type: user
paper_authors.user: KianYale
paper_authors._id: 67cd7ace999766d8cd73fb19
paper_authors.name: Yinghao Cai
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-11T14:12:56.722Z
paper_authors._id: 64f53882bc54e7f4eeede46a
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/64f53882bc54e7f4eeede46a/R43aKnfpmyHtUbwZWT64p.jpeg
paper_authors.fullname: Yinghao Cai
paper_authors.type: user
paper_authors.user: paparika
paper_authors._id: 67cd7ace999766d8cd73fb1a
paper_authors.name: Chao Li
paper_authors._id: 67cd7ace999766d8cd73fb1b
paper_authors.name: Wenzhe He
paper_authors._id: 67cd7ace999766d8cd73fb1c
paper_authors.name: Xudong Zheng
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-11T14:13:12.216Z
paper_authors._id: 65638259330b667bb2416ab9
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/IuOfh4z_hDuJ3XL0jqvVx.png
paper_authors.fullname: Xudong Zheng
paper_authors.type: user
paper_authors.user: xudongz
paper_authors._id: 67cd7ace999766d8cd73fb1d
paper_authors.name: Tao Lu
paper_authors._id: 67cd7ace999766d8cd73fb1e
paper_authors.name: Bin Liang
paper_authors._id: 67cd7ace999766d8cd73fb1f
paper_authors.name: Shuo Wang
paper_title: NeuGrasp: Generalizable Neural Surface Reconstruction with Background
    Priors for Material-Agnostic Object Grasp Detection
paper_summary: 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/.
paper_ai_keywords: neural surface reconstruction background priors material-agnostic grasp detection transformers global prior volumes multi-view features spatial encoding narrow and sparse viewing conditions residual feature enhancement occupancy-prior volume transparent objects specular surfaces