Jigsaw: Learning to Assemble Multiple Fractured Objects
Authors: Jiaxin Lu, Yifan Sun, Qixing Huang
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate Jigsaw on the Breaking Bad dataset and achieve superior performance compared to state-of-the-art methods. |
| Researcher Affiliation | Academia | Jiaxin Lu Yifan Sun Qixing Huang Department of Computer Science University of Texas at Austin {lujiaxin, yifansun12}@utexas.edu huangqx@cs.utexas.edu |
| Pseudocode | No | The paper describes the method components and pipeline in text and diagrams (Figure 1), but it does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our code is available at https://jiaxin-lu.github.io/Jigsaw/. |
| Open Datasets | Yes | We leverage the Breaking Bad dataset (Sellan et al., 2022), a novel data set of multiple fracture assemblies featuring synthetic physical breaking patterns. |
| Dataset Splits | No | Everyday consists of 498 models and 41,754 fracture patterns, and is split into a training set (34,075 fracture patterns from 407 objects) and a test set (7,679 fracture patterns from 91 objects). The paper specifies training and test splits but does not explicitly define a separate validation split. |
| Hardware Specification | Yes | All experiments are conducted on a Linux workstation with 4 Tesla V100-SXM2-32GB GPUs, Intel(R) Xeon(R) CPU E5-2698 v4 @ 2.20GHz CPUs, and 480GB Memory. |
| Software Dependencies | No | The paper states 'Our framework is implemented in Pytorch' but does not provide specific version numbers for PyTorch or any other software dependencies used in the experiments. |
| Experiment Setup | Yes | Table 1 lists detailed experiment parameters including training epochs (250), batch size (4), learning rate (0.001), optimizer (Adam), scheduler (Cosine), minimum learning rate (1e-5), loss ratios (α, β, γ), and temperature parameter (τ). Additionally, it states: 'For Jigsaw, we start training with only the segmentation loss. We add matching loss after first 10 epochs, and rigidity loss after 200 epochs.' |