Sensitivity in Translation Averaging
Authors: Lalit Manam, Venu Madhav Govindu
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 6 Experiments. We consider Sf M datasets provided in 1DSf M [55] for the experiments. ... In Table 1, we list the number of nodes and edges removed due to the removal of skewed triangles and check the absolute translation errors obtained using BATA. |
| Researcher Affiliation | Academia | Lalit Manam Indian Institute of Science Bengaluru, India 560012 lalitmanam@iisc.ac.in Venu Madhav Govindu Indian Institute of Science Bengaluru, India 560012 venug@iisc.ac.in |
| Pseudocode | Yes | Algorithm 1: Removal of Skewed Triangles from Sparse Networks |
| Open Source Code | No | The paper states 'Our code is implemented in MATLAB.' but does not provide any link or explicit statement about releasing the source code for the described methodology. |
| Open Datasets | Yes | We consider Sf M datasets provided in 1DSf M [55] for the experiments. |
| Dataset Splits | No | The paper mentions using 'Sf M datasets' but does not provide specific details on training, validation, or test dataset splits (e.g., percentages, sample counts, or predefined split references). |
| Hardware Specification | Yes | All experiments are performed on a PC with Intel Xeon Silver 4210 processor with 128 GB RAM. |
| Software Dependencies | No | The paper states 'Our code is implemented in MATLAB.' but does not provide a specific version number for MATLAB or any other software dependencies. |
| Experiment Setup | Yes | Now, we use Algo. 1 to remove skewed triangles (minimum angle < 5 ) and denote the output as the filtered network and compare the solutions from the two networks. |