Dynamic to Static Lidar Scan Reconstruction Using Adversarially Trained Auto Encoder
Authors: Prashant Kumar, Sabyasachi Sahoo, Vanshil Shah, Vineetha Kondameedi, Abhinav Jain, Akshaj Verma, Chiranjib Bhattacharyya, Vinay Vishwanath1836-1844
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our proposed approaches against baselines on three different datasets with the following goals: (1) In section 4, we evaluate our proposed approaches with adapted baseline models for the problem of DST for Li DAR, (2) In section 4, we evaluate our proposed approaches DSLR, DSLR-Seg, and DSLR-UDA, for Li DAR based SLAM. Experiments on simulated and real-world datasets show that DSLR gives at least a 4 improvement over adapted baselines. |
| Researcher Affiliation | Collaboration | 1Indian Institute of Science, Bangalore, India 2AMIDC Pvt Ltd, Bangalore, India 3Chennai Mathematical Institute, Chennai, India {prshnttkmr, ssahoo.iisc, vanshilshah, vineetha.knd92, abhinav98jain, akshajverma7}@gmail.com, chiru@iisc.ac.in, vinay@atimotors.com |
| Pseudocode | No | The paper does not contain any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | Yes | Code, Dataset and Appendix: https://dslrproject.github.io/dslr/ |
| Open Datasets | Yes | We open-source 2 new datasets, CARLA-64, ARD16 (Ati Realworld Dataset) consisting of corresponding static-dynamic Li DAR scan pairs for simulated and real world scenes respectively. ... KITTI-64 dataset: To show results on a standard real-world dataset, we use the KITTI odometry dataset (Geiger, Lenz, and Urtasun 2012), which contains segmentation information and ground truth poses. |
| Dataset Splits | No | The paper mentions training and testing on datasets but does not provide explicit training/validation/test dataset splits with percentages or sample counts. |
| Hardware Specification | No | The paper does not explicitly describe the hardware (e.g., specific GPU or CPU models) used for running its experiments. |
| Software Dependencies | No | The paper mentions software components like DCGAN, U-Net, Open3D, and Cartographer, but does not provide specific version numbers for these tools or any other software dependencies. |
| Experiment Setup | Yes | We change the model architecture to a 40 512 grid instead of a 64 1024 grid. This is done to discard the outer circles in a Li DAR scan because it contains the most noise and have least information about the scene. The bottleneck dimension of our model is 160. ... 휆is a constant which is set to 0.01. |