We lifted 10 functions out of this paper's own repositories and ran 6 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| umass-foundation-model/3d-llm | pwc_unofficial | 5 of 9 |
| copy not recorded | — | 1 of 1 |
| Function | Status | Where it lives |
|---|---|---|
| compress | Ran | umass-foundation-model/3d-llm/3DLanguage_data/ChatCaptioner_based/quetion_model.py pointer only (licence: MIT) · get_code("80dde39da1ba3708") |
| disabled_train | Ran | this paper's copy was not recorded; identical code first harvested from zhouc20/LatentGraphDiffusion pointer only · get_code("4cb732f513d69dfd") |
| interpolate_pos_embed | Ran | umass-foundation-model/3d-llm/3DLLM_BLIP2-base/lavis/models/vit.py pointer only (licence: MIT) · get_code("c6ec173f19f5c34d") |
| processDigitArticle | Ran | umass-foundation-model/3d-llm/3DLLM_BLIP2-base/calculate_scores/calculate_score_3dmvvqa.py pointer only (licence: MIT) · get_code("4e436e13094b20cb") |
| processPunctuation | Ran | umass-foundation-model/3d-llm/3DLLM_BLIP2-base/calculate_scores/calculate_score_3dmvvqa.py pointer only (licence: MIT) · get_code("35e84a8da1d4f427") |
| tile | Ran | umass-foundation-model/3d-llm/3DLLM_BLIP2-base/lavis/models/base_model.py pointer only (licence: MIT) · get_code("cb33571427334815") |
| all_gather_with_grad | Not yet run | umass-foundation-model/3d-llm/3DLLM_BLIP2-base/lavis/models/base_model.py pointer only (licence: MIT) · get_code("0ec9fc2025c16f65") |
| decompress | Not yet run | umass-foundation-model/3d-llm/3DLanguage_data/ChatCaptioner_based/quetion_model.py pointer only (licence: MIT) · get_code("a2b79de775636d17") |
| get_gpu_memory | Not yet run | umass-foundation-model/3d-llm/3DLanguage_data/ChatCaptioner_based/quetion_model.py pointer only (licence: MIT) · get_code("fe66d0280e436155") |
| get_lemma | Not yet run | umass-foundation-model/3d-llm/3DLLM_BLIP2-base/calculate_scores/calculate_score_3dmvvqa.py pointer only (licence: MIT) · get_code("a1ff41c753bffe5d") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
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. Project Page: : https://vis-www.cs.umass.edu/3dllm/.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2307.12981")
get_code_for_paper("2307.12981")
have("2307.12981")
Connect an agent — have() is free.