Sun Apr 23 2023
Fri Apr 21 2023

Anything-3D: Towards Single-view Anything Reconstruction in the Wild

object segmentation
3D reconstruction
computer vision
real estate
interior design
product design and prototyping

This paper introduces Anything-3D, a framework that combines various visual-language models and object segmentation to elevate objects to 3D, yielding a reliable and versatile system for the single-view conditioned 3D reconstruction task. The approach demonstrates its ability to produce accurate and detailed 3D reconstructions for a wide array of objects and shows promise in addressing the limitations of existing methodologies.

This framework can be used in businesses that need to accurately and quickly produce 3D reconstructions from single-view images, such as in real estate or interior design. The approach can also be used for product design and prototyping, allowing businesses to more easily create and visualize 3D models.

Theory on Adam Instability in Large-Scale Machine Learning

optimization algorithms
large-scale machine learning
language models

This paper presents a theory for the previously unexplained divergent behavior noticed in the training of large language models. The researchers argue that the phenomenon is an artifact of the dominant optimization algorithm used for training, called Adam. They observe that Adam can enter a state in which the parameter update vector has a relatively large norm and is essentially uncorrelated with the direction of descent on the training loss landscape, leading to divergence.

This paper helps businesses understand the optimization algorithms used in large-scale machine learning, specifically in language models. Businesses that use language models for their operations can use this paper to troubleshoot divergent behavior in training and improve their models' performance.

Is ChatGPT a Good Recommender? A Preliminary Study

conversational models
natural language processing
recommendation systems

This paper explores the potential of ChatGPT, a conversational model used in natural language processing, as a general-purpose recommendation model. The researchers design a set of prompts and evaluate ChatGPT's performance on five recommendation scenarios, achieving promising results in certain tasks and reaching baseline level in others. They also conduct human evaluations on two explainability-oriented tasks to more accurately evaluate the quality of contents generated by different models.

This paper helps businesses understand the potential of ChatGPT as a recommendation system, utilizing its linguistic and world knowledge acquired from large-scale corpora to improve businesses' recommendation performance. Businesses can use this paper to explore and implement ChatGPT in their recommendation systems.

Nerfbusters: Removing Ghostly Artifacts from Casually Captured NeRFs

Neural Radiance Fields
Image Processing
Deep Learning
3D modeling
Virtual Reality
Computer Vision

Casually captured Neural Radiance Fields (NeRFs) suffer from artifacts such as floaters or flawed geometry when rendered outside the camera trajectory. Existing evaluation protocols often do not capture these effects, since they usually only assess image quality at every 8th frame of the training capture.

The proposed 3D diffusion-based method leverages local 3D priors and a novel density-based score distillation sampling loss to discourage artifacts during NeRF optimization, removing floaters and improving scene geometry for casual captures. This can benefit businesses that use NeRFs in their processes, such as 3D modeling or virtual reality.

Thu Apr 20 2023
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