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Looks like Flutter is slowly getting better and better at supporting 3D rendering! This PR adds support for rendering 3D models directly in Flutter: Video and PR are done by Brandon DeRosier, keep up the awesome work 🤩

86,470 次观看 • 3 年前 •via X (Twitter)

10 条评论

Brandon DeRosier 的头像
Brandon DeRosier3 年前

Thanks for the shout out! This is an early experiment to integrate Impeller Scene in the framework. With the advent of Impeller, it's easier than ever to rapidly build out high value rendering features like this. IMO, Flutter is just getting started. The future is bright!

Stephan E.G. Veenstra 💙 的头像
Stephan E.G. Veenstra 💙3 年前

@algebrandon Great, more things that can distract me from my actual tasks.😅

Rutvik Tak 💙 的头像
Rutvik Tak 💙3 年前

@algebrandon This may just let me get back working with @Blender 😍 Super hyped to check on this🙌

Paras Jain 💙 的头像
Paras Jain 💙3 年前

@_eseidel @algebrandon 🙌

Godwin of the future 的头像
Godwin of the future3 年前

@onuoha_ifeanyi @algebrandon Flutter may just be the next best language after JavaScript. I love it!!

un vato.eth 的头像
un vato.eth3 年前

@algebrandon Eso lo hace three js

Ravin Sharma 的头像
Ravin Sharma3 年前

@algebrandon My gawd. This is so so good. Any docs to try this out. I'm gonna try it in an app.

wolfenra.in 🦄💙🏳️‍🌈 的头像
wolfenra.in 🦄💙🏳️‍🌈3 年前

@algebrandon This got merged to master just a few hours ago, it is nowhere near the point that you can actively use it in your apps

czar 的头像
czar3 年前

@algebrandon I’m currently investing my time into 3Ds using Flutter…

wolfenra.in 🦄💙🏳️‍🌈 的头像
wolfenra.in 🦄💙🏳️‍🌈3 年前

@algebrandon It will be time for it soon 🫡

相关视频

3D-LLM: Injecting the 3D World into Large Language Models paper page: Large language models (LLMs) and Vision-Language Models (VLMs) have been proven to excel at multiple tasks, such as commonsense reasoning. Powerful as these models can be, they are not grounded in the 3D physical world, which involves richer concepts such as spatial relationships, affordances, physics, layout, and so on. In this work, we propose to inject the 3D world into large language models and introduce a whole new family of 3D-LLMs. Specifically, 3D-LLMs can take 3D point clouds and their features as input and perform a diverse set of 3D-related tasks, including captioning, dense captioning, 3D question answering, task decomposition, 3D grounding, 3D-assisted dialog, navigation, and so on. Using three types of prompting mechanisms that we design, we are able to collect over 300k 3D-language data covering these tasks. To efficiently train 3D-LLMs, we first utilize a 3D feature extractor that obtains 3D features from rendered multi- view images. Then, we use 2D VLMs as our backbones to train our 3D-LLMs. By introducing a 3D localization mechanism, 3D-LLMs can better capture 3D spatial information. Experiments on ScanQA show that our model outperforms state-of-the-art baselines by a large margin (e.g., the BLEU-1 score surpasses state-of-the-art score by 9%). Furthermore, experiments on our held-in datasets for 3D captioning, task composition, and 3D-assisted dialogue show that our model outperforms 2D VLMs. Qualitative examples also show that our model could perform more tasks beyond the scope of existing LLMs and VLMs.

AK

249,708 次观看 • 3 年前