Recent advances in neural radiance fields and its variants primarily address opaque or transparent objects, encountering difficulties to reconstruct both transparent and opaque objects simultaneously. This paper introduces \alpha -NeuS \textemdash an extension of NeuS \textemdash that proves NeuS is unbiased for materials from fully transparent to fully opaque. We find that transparent and opaque surfaces align with the non-negative local minima and the zero iso-surface, respectively, in the learned distance field of NeuS. Traditional iso-surfacing extraction algorithms, such as marching cubes, which rely on fixed iso-values, are ill-suited for such data. We develop a method to extract the transparent and opaque surface simultaneously based on DCUDF.

Opaque, a new startup born out of Berkely's RISELabs, announced a $9.5 million seed round today to build a solution to access and work with sensitive data in the cloud in a secure way, even with multiple organizations involved. Intel Capital led today's investment with participation by Race Capital, The House Fund and FactoryHQ. The company helps customers work with secure data in the cloud while making sure the data they are working on is not being exposed to cloud providers, other research participants or anyone else, says company president Raluca Ada Popa. "What we do is we use this very exciting hardware mechanism called Enclave, which [operates] deep down in the processor -- it's a physical black box -- and only gets decrypted there. Company co-founder Ion Stoica, who was a co-founder at Databricks, says the startup's solution helps resolve two conflicting trends. On one hand, businesses increasingly want to make use of data, but at the same time are seeing a growing trend toward privacy. Opaque is designed to resolve this by giving customers access to their data in a safe and fully encrypted way. Data is the world's most valuable (and vulnerable) resource The company describes the solution as "a novel combination of two key technologies layered on top of state-of-the-art cloud security--secure hardware enclaves and cryptographic fortification." This enables customers to work with data -- for example to build machine learning models -- without exposing the data to others, yet while generating meaningful results. Popa says this could be helpful for hospitals working together on cancer research, who want to find better treatment options without exposing a given hospital's patient data to other hospitals, or banks looking for money laundering without exposing customer data to other banks, as a couple of examples. Investors were likely attracted to the pedigree of Popa, a computer security and applied crypto professor at UC Berkeley and Stoica, who is also a Berkeley professor and co-founded Databricks. Both helped found RISELabs at Berkeley where they developed the solution and spun it out as a company. Mark Rostick, vice president and senior managing director at lead investor Intel Capital says his firm has been working with the founders since the startup's earliest days, recognizing the potential of this solution to help companies find complex solutions even when there are multiple organizations involved sharing sensitive data. "Enterprises struggle to find value in data across silos due to confidentiality and other concerns.

Where does your enterprise stand on the AI adoption curve? Take our AI survey to find out. Opaque, a startup that helps organizations analyze encrypted data in the cloud, today announced that it closed a $9.5 million seed funding round led by Intel Capital with contributions from Race Capital, The House Fund, and FactoryHQ. Cofounder Raluca Ada Popa says that the funds will help to expand Opaque's ongoing contributions to the open source and data security communities. A majority of data sits in private hands.