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Datasets:
qizekun
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3D-MM-Vet

Tasks:
Question Answering
Languages:
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The dataset viewer is not available for this dataset.
Cannot get the config names for the dataset.
Error code:   ConfigNamesError
Exception:    DataFilesNotFoundError
Message:      No (supported) data files found in qizekun/3D-MM-Vet
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/dataset/config_names.py", line 67, in compute_config_names_response
                  get_dataset_config_names(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 347, in get_dataset_config_names
                  dataset_module = dataset_module_factory(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1873, in dataset_module_factory
                  raise e1 from None
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1854, in dataset_module_factory
                  return HubDatasetModuleFactoryWithoutScript(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1245, in get_module
                  module_name, default_builder_kwargs = infer_module_for_data_files(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 595, in infer_module_for_data_files
                  raise DataFilesNotFoundError("No (supported) data files found" + (f" in {path}" if path else ""))
              datasets.exceptions.DataFilesNotFoundError: No (supported) data files found in qizekun/3D-MM-Vet

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3D MM-Vet

3D MM-Vet is a carefully crafted multi-level 3D QA benchmark that consists of 59 unique 3D models and 232 human-written questions and answers with rich content.

The test data and scripts have been uploaded to Hugging Face. You can also locate the evaluation scripts from the codebase of ShapeLLM.

Furthermore, we propose 3D MM-Vet-C, which contains three variants: single-view, jitter, and rotation. They represent extracting partial point clouds of the front view field of view, adding Gaussian noise to the point cloud xyz, and random rotation on the x, y, and z axes, respectively.

Here is a more detailed explanation of each variant:

  • Single-view: This variant focuses on the model's ability to understand the 3D object from a single viewpoint. To create the single-view variant, we extract the front-view point cloud of each model.
  • Jitter: This variant tests the model's robustness to noise. To create the jitter variant, we add Gaussian noise with zero mean and variance of 0.01 to the point cloud xyz.
  • Rotation: This variant examines the model's ability to understand the 3D scene from different viewpoints. To create the rotation variant, we randomly apply 30 degrees of random rotation on the x, y, and z axes.

We believe that 3D MM-Vet and 3D MM-Vet-C are valuable resources for the 3D QA community. They can be used to evaluate the performance of existing models and to develop new models that are better at understanding and reasoning about 3D objects.

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