Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
OGC: Unsupervised 3D Object Segmentation from Rigid Dynamics of Point Clouds
Authors: Ziyang Song, Bo Yang
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We extensively evaluate our method on five datasets, demonstrating the superior performance for object part instance segmentation and general object segmentation in both indoor and the challenging outdoor scenarios. Our code and data are available at https://github.com/v LAR-group/OGC |
| Researcher Affiliation | Academia | v LAR Group, The Hong Kong Polytechnic University EMAIL EMAIL |
| Pseudocode | Yes | Algorithm 1 Iterative optimization of object segmentation and scene flow estimation. Assume the whole train split has S point cloud pairs: {(P t, P t+1)1 (P t, P t+1)S}. |
| Open Source Code | Yes | Our code and data are available at https://github.com/v LAR-group/OGC |
| Open Datasets | Yes | Our code and data are available at https://github.com/v LAR-group/OGC |
| Dataset Splits | Yes | In total, we create 3750, 250, and 1000 indoor rooms (scenes) for training/validation/test splits. |
| Hardware Specification | Yes | The scene flow network is trained for 200 epochs... using a single NVIDIA A100 GPU. |
| Software Dependencies | Yes | Our code is written in Python 3.8 and PyTorch 1.9. |
| Experiment Setup | Yes | We train the object segmentation network for 100 epochs, with Adam optimizer (β1=0.9, β2=0.999), an initial learning rate of 0.001 and batch size of 8. The scene flow network is trained for 200 epochs with Adam optimizer, an initial learning rate of 0.001 and batch size of 4. |