Translation Synchronization via Truncated Least Squares
Authors: Xiangru Huang, Zhenxiao Liang, Chandrajit Bajaj, Qixing Huang
NeurIPS 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on synthetic and real datasets show that Tran Sync is superior to state-of-the-art convex formulations in terms of both efficiency and accuracy. |
| Researcher Affiliation | Academia | Xiangru Huang The University of Texas at Austin 2317 Speedway, Austin, 778712 xrhuang@cs.utexas.edu Zhenxiao Liang Tsinghua University Beijing, China, 100084 liangzx14@mails.tsinghua.edu.cn Chandrajit Bajaj The University of Texas at Austin 2317 Speedway, Austin, 778712 bajaj@cs.utexas.edu Qixing Huang The University of Texas at Austin 2317 Speedway, Austin, 778712 huangqx@cs.utexas.edu |
| Pseudocode | Yes | Algorithm 1 Tran Sync(c, kmax) |
| Open Source Code | No | The paper does not provide any specific links or explicit statements about releasing source code for the described methodology. |
| Open Datasets | Yes | We utilize the Patriot Circle Lidar dataset1. 1http://masc.cs.gmu.edu/wiki/Map GMU |
| Dataset Splits | No | The paper describes the stopping condition for its iterative algorithm, but it does not specify explicit training, validation, or test dataset splits. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used to run the experiments (e.g., CPU, GPU models, memory). |
| Software Dependencies | No | The paper does not provide specific version numbers for any software dependencies used in the experiments. |
| Experiment Setup | Yes | For this experiment, instead of using kmax as stopping condition as in Algorithm 1, we stop when we observe δk < δmin. Here δmin does not need to be close to σ. In fact, we choose δmin = 0.05, 0.1 for σ = 0.01, 0.04, respectively. |