Point-E Description
Recent advancements in text-based 3D object generation have yielded encouraging outcomes; however, leading methods generally need several GPU hours to create a single sample, which is a stark contrast to the latest generative image models capable of producing samples within seconds or minutes. In this study, we present a different approach to generating 3D objects that enables the creation of models in just 1-2 minutes using a single GPU. Our technique initiates by generating a synthetic view through a text-to-image diffusion model, followed by the development of a 3D point cloud using a second diffusion model that relies on the generated image for conditioning. Although our approach does not yet match the top-tier quality of existing methods, it offers a significantly faster sampling process, making it a valuable alternative for specific applications. Furthermore, we provide access to our pre-trained point cloud diffusion models, along with the evaluation code and additional models, available at this https URL. This contribution aims to facilitate further exploration and development in the realm of efficient 3D object generation.
Point-E Alternatives
PackageX OCR Scanning
PackageX OCR API turns any smartphone into an incredibly powerful universal label scanner. It can read every bit of text, including barcodes, QR codes and other information on the label.
Our OCR technology is the best in the industry. It uses proprietary algorithms and deep learning models to extract information from labels.
Our OCR API has been trained using information from more than 10 million labels. This allows for the highest scanning accuracy in the market, at over 95%.
Our technology can scan in low-light conditions and read labels from any angle.
Create your own OCR scanner app to eliminate pen-and-paper inefficiencies.
Our OCR scanner allows you to extract information from printed text or handwritten labels.
Our OCR software is trained using multilingual label data extracted in over 40 countries.
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QBench
QBench allows you to keep track of all your samples and where they are located in the workflow using a single system. QBench eliminates the need for spreadsheets, shared folders in the network, and paper-based tracking systems. You can view hundreds of PDF reports/COAs before publishing or emailing. You can generate barcodes and create labels that you can customize for your samples. Compatible with standard printers and scanners. QBench's billing module allows you to create and send invoices right from the system. You can see counts and latencies for different data types in QBench. This includes metrics like turnaround time, sample counts per test, sample delay, and many others. QBench makes it easy for you to gather the data your lab needs for the assays you perform.
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ModelsLab
ModelsLab is a groundbreaking AI firm that delivers a robust array of APIs aimed at converting text into multiple media formats, such as images, videos, audio, and 3D models. Their platform allows developers and enterprises to produce top-notch visual and audio content without the hassle of managing complicated GPU infrastructures. Among their services are text-to-image, text-to-video, text-to-speech, and image-to-image generation, all of which can be effortlessly integrated into a variety of applications. Furthermore, they provide resources for training customized AI models, including the fine-tuning of Stable Diffusion models through LoRA methods. Dedicated to enhancing accessibility to AI technology, ModelsLab empowers users to efficiently and affordably create innovative AI products. By streamlining the development process, they aim to inspire creativity and foster the growth of next-generation media solutions.
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DreamFusion
Recent advancements in the realm of text-to-image synthesis have emerged from diffusion models that have been trained on vast amounts of image-text pairs. To successfully transition this methodology to 3D synthesis, it would necessitate extensive datasets of labeled 3D assets alongside effective architectures for denoising 3D information, both of which are currently lacking. In this study, we address these challenges by leveraging a pre-existing 2D text-to-image diffusion model to achieve text-to-3D synthesis. We propose a novel loss function grounded in probability density distillation that allows a 2D diffusion model to serve as a guiding principle for the optimization of a parametric image generator. By implementing this loss in a DeepDream-inspired approach, we refine a randomly initialized 3D model, specifically a Neural Radiance Field (NeRF), through gradient descent to ensure its 2D renderings from various angles exhibit a minimized loss. Consequently, the 3D representation generated from the specified text can be observed from multiple perspectives, illuminated with various lighting conditions, or seamlessly integrated into diverse 3D settings. This innovative method opens new avenues for the application of 3D modeling in creative and commercial fields.
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Company Details
Company:
OpenAI
Year Founded:
2015
Headquarters:
United States
Website:
openai.com/research/point-e
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