Loading video...

Video Failed to Load

Go Home

๐Ÿš€ Our NeurIPS '24 work, Large Spatial Model (LSM), is here! LSM performs semantic 3D reconstruction in just 0.1s, processing unposed data via feed-forward 3D reconstruction. ๐Ÿ‘‰It leverages large-scale 3D datasets with minimal annotations, defining a 3D latent space. We are continuously exploring how this explicit 3D representation can...

43,826 views โ€ข 1 year ago โ€ขvia X (Twitter)

4 Comments

Zhiwen(Aaron) Fan's profile picture
Zhiwen(Aaron) Fan1 year ago

Itโ€™s been an unforgettable collaboration with everyone as we discussed and converged on this exciting direction! None of this would have been possible without each of you. Looking forward to expanding the #LargeSpatialModelโ€™s capabilities even further soon! Jian Zhang, @CongWenyan0320 , @peihao_wang, Renjie Li, @KairunWen , @ShijieZhoucla , @AchutaKadambi , Zhangyang Wang, @danfei_xu , @iamborisi , @drmapavone , @yuewang314

Boe's profile picture
Boe1 year ago

The demo page doesnโ€™t let you upload any kind of photos on iOS. The only input method is through the files app, and .png, .jpg, .heif are not supported.

berkshiremystery's profile picture
berkshiremystery1 year ago

Seems good designed to work in the #PLTR AIP ontology, as an application ๐Ÿคซ๐Ÿค”๐Ÿ’ญ๐Ÿ™‰ augmenting reality into simulation ๐Ÿค—๐Ÿš€ @chadwahl @david_marra

Michael Yuan's profile picture
Michael Yuan1 year ago

wow

Related Videos

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

249,494 views โ€ข 2 years ago