Saturday, June 15, 2024

Artificial Intelligence news

How to opt out...

MIT Technology Review’s How To series helps you get things done.  If you...

Apple is promising personalized...

At its Worldwide Developer Conference on Monday, Apple for the first time...

What using artificial intelligence...

This story originally appeared in The Algorithm, our weekly newsletter on AI....

The data practitioner for...

The rise of generative AI, coupled with the rapid adoption and democratization...
HomeMachine LearningFineRecon: Depth-aware Feed-forward...

FineRecon: Depth-aware Feed-forward Network for Detailed 3D Reconstruction



Recent works on 3D reconstruction from posed images have demonstrated that direct inference of scene-level 3D geometry without iterative optimization is feasible using a deep neural network, showing remarkable promise and high efficiency. However, the reconstructed geometries, typically represented as a 3D truncated signed distance function (TSDF), are often coarse without fine geometric details. To address this problem, we propose three effective solutions for improving the fidelity of inference-based 3D reconstructions. We first present a resolution-agnostic TSDF supervision strategy to…



Article Source link and Credit

Continue reading

ContextQ: Generated Questions to Support Meaningful Parent-Child Dialogue While Co-Reading

Much of early literacy education happens at home with caretakers reading books to young children. Prior research demonstrates how having dialogue with children during co-reading can develop critical reading readiness skills, but most adult readers are unsure if...

On Efficient and Statistical Quality Estimation for Data Annotation

Annotated data is an essential ingredient to train, evaluate, compare and productionalize machine learning models. It is therefore imperative that annotations are of high quality. For their creation, good quality management and thereby reliable quality estimates are needed....

Swallowing the Bitter Pill: Simplified Scalable Conformer Generation

We present a novel way to predict molecular conformers through a simple formulation that sidesteps many of the heuristics of prior works and achieves state of the art results by using the advantages of scale. By training a...