Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Tracklet Proposal Network for Multi-Object Tracking on Point Clouds
Authors: Hai Wu, Qing Li, Chenglu Wen, Xin Li, Xiaoliang Fan, Cheng Wang
IJCAI 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | On the KITTI tracking benchmark, it attains an MOTA of 91.75%, outperforming all submitted results on the online leaderboard. |
| Researcher Affiliation | Academia | 1School of Informatics, Xiamen University 2School of Electrical Engineering and Computer Science, Louisiana State University |
| Pseudocode | No | The paper describes the algorithms in prose but does not include any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | Yes | We evaluate our method on the KITTI tracking dataset [Geiger et al., 2012], which consists of 21 training sequences and 29 test sequences. |
| Dataset Splits | Yes | For the ablation study, we split the KITTI training set into two sub-datasets for training and validation. The sub training set consists of 11 sequences, and the validation set consists of 10 sequences. |
| Hardware Specification | Yes | We adopted the ADAM optimizer with batch size 4, learning rate 0.01 and 80 epochs on two RTX 2080 Ti GPUs to train our PC-TCNN. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. |
| Experiment Setup | Yes | We adopted the ADAM optimizer with batch size 4, learning rate 0.01 and 80 epochs on two RTX 2080 Ti GPUs to train our PC-TCNN. |