Learning Superpoint Graph Cut for 3D Instance Segmentation
Authors: Le Hui, Linghua Tang, Yaqi Shen, Jin Xie, Jian Yang
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on two challenging datasets, Scan Net v2 and S3DIS, show that our method achieves new state-of-the-art performance on 3D instance segmentation. Code is available at https://github.com/fpthink/Graph Cut. |
| Researcher Affiliation | Academia | Le Hui , Linghua Tang , Yaqi Shen, Jin Xie , Jian Yang PCA Lab, Nanjing University of Science and Technology, China {le.hui, tanglinghua, syq, csjxie, csjyang}@njust.edu.cn |
| Pseudocode | Yes | Algorithm 1 Proposal Generation Algorithm Input: node semantic scores S = {s1, . . . , s|V | | si RN for i = 1, . . . , |V |}, N is the number of classes; semantic threshold θ; edge scores A = {au,v} R|E| 1, au,v indicates the score of edge which connects nodes u and v; Output: proposals I = {I1, . . . , Im}, m is the number of proposals. |
| Open Source Code | Yes | Code is available at https://github.com/fpthink/Graph Cut. |
| Open Datasets | Yes | Datasets. We conduct experiments on two benchmark datasets, Scan Net v2 [7] and S3DIS [1]. |
| Dataset Splits | Yes | The Scan Net v2 dataset contains 1,613 3D scenes, which are split into 1,201 training, 312 validation, and 100 test scenes, respectively. |
| Hardware Specification | Yes | Our model is trained on a single TITAN RTX GPU. |
| Software Dependencies | No | The paper mentions using the Adam optimizer and submanifold sparse convolution, but it does not specify any software names with version numbers (e.g., PyTorch, TensorFlow, CUDA versions). |
| Experiment Setup | Yes | Our model is trained on a single TITAN RTX GPU. We use the Adam optimizer with a base learning rate of 0.001 for the network training, which is scheduled by a cosine annealing. The voxel size is set to 0.02m. ... At training time, we limit the maximum number of points in a scene to 250k and crop the excess randomly. Due to the high point density of S3DIS, we randomly downsample its 1/4 points before cropping. |