NeuGrasp: Generalizable Neural Surface Reconstruction with Background
  Priors for Material-Agnostic Object Grasp Detection
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/.
67cd7ace999766d8cd73fb18
Qingyu Fan
claimed_verified
2025-03-10T08:01:41.233Z
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Fan
user
KianYale
67cd7ace999766d8cd73fb19
Yinghao Cai
admin_assigned
2025-03-11T14:12:56.722Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/64f53882bc54e7f4eeede46a/R43aKnfpmyHtUbwZWT64p.jpeg
Yinghao Cai
user
paparika
67cd7ace999766d8cd73fb1a
Chao Li
67cd7ace999766d8cd73fb1b
Wenzhe He
67cd7ace999766d8cd73fb1c
Xudong Zheng
admin_assigned
2025-03-11T14:13:12.216Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/IuOfh4z_hDuJ3XL0jqvVx.png
Xudong Zheng
user
xudongz
67cd7ace999766d8cd73fb1d
Tao Lu
67cd7ace999766d8cd73fb1e
Bin Liang
67cd7ace999766d8cd73fb1f
Shuo Wang
NeuGrasp: Generalizable Neural Surface Reconstruction with Background
  Priors for Material-Agnostic Object Grasp Detection
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/.
NeuGrasp, a neural surface reconstruction method using transformers and global prior volumes, enhances grasping of transparent and specular objects by aggregating multi-view features and refining spatial perception.
neural surface reconstruction transformers global prior volumes multi-view features spatial encoding residual feature enhancement occupancy-prior volume