Neural Unsigned Distance Fields for Implicit Function Learning Julian Chibane Aymen Mir Gerard Pons-Moll
–Neural Information Processing Systems
In this work we target a learnable output representation that allows continuous, high resolution outputs of arbitrary shape.
Neural Information Processing Systems
Nov-15-2025, 16:32:01 GMT
- Country:
- Asia
- China > Shandong Province
- Qingdao (0.04)
- Japan > Honshū
- Chūbu > Ishikawa Prefecture > Kanazawa (0.04)
- China > Shandong Province
- Europe
- Germany
- Bavaria > Upper Bavaria
- Munich (0.04)
- Saarland (0.04)
- Bavaria > Upper Bavaria
- Italy > Veneto
- Venice (0.04)
- Netherlands > North Holland
- Amsterdam (0.04)
- Spain > Catalonia
- Barcelona Province > Barcelona (0.04)
- Germany
- North America
- Canada
- British Columbia > Metro Vancouver Regional District
- Vancouver (0.04)
- Quebec > Montreal (0.04)
- British Columbia > Metro Vancouver Regional District
- United States
- California
- Los Angeles County > Long Beach (0.05)
- Orange County > Anaheim (0.04)
- Hawaii > Honolulu County
- Honolulu (0.05)
- Louisiana > Orleans Parish
- New Orleans (0.04)
- Massachusetts > Suffolk County
- Boston (0.04)
- Utah > Salt Lake County
- Salt Lake City (0.04)
- California
- Canada
- Asia
- Technology: