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..
Multi-scale Consistency for Robust 3D Registration via Hierarchical Sinkhorn Tree
Authors: Chengwei Ren, Yifan Feng, Weixiang Zhang, Xiao-Ping (Steven) Zhang, Yue Gao
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments demonstrate HST consistently outperforms the state-of-the-art methods on both indoor and outdoor benchmarks. |
| Researcher Affiliation | Academia | {1Shenzhen Ubiquitous Data Enabling Key Lab, 2Shenzhen International Graduate School, 3BNRist, THUIBCS, School of Software}, Tsinghua University |
| Pseudocode | No | The paper describes the algorithm steps in text and figures (e.g., Figure 2 for overview, Section 3.2 for Hierarchical Sinkhorn Tree), but it does not include any explicitly labeled 'Pseudocode' or 'Algorithm' blocks. |
| Open Source Code | Yes | Question: Does the paper provide open access to the data and code, with sufficient instructions to faithfully reproduce the main experimental results, as described in supplemental material? Answer: [Yes] Justification: Please see the supplemental material Sec. A.4. |
| Open Datasets | Yes | 3DMatch [1] contains 62 scenes collected from SUN3D [2], 7-Scenes [3], RGBD Scenes v.2 [4], Analysis-by-Synthesis [5], Bundle Fusion [6], and Halbel et al. [7] among which 46 scenes are used for training, 8 scenes for validation and 8 scenes for testing. KITTI [11] ... adopt 0-5 for training, 6-7 for validation and 8-10 for testing. |
| Dataset Splits | Yes | 3DMatch [1] ... 46 scenes are used for training, 8 scenes for validation and 8 scenes for testing. KITTI [11] ... adopt 0-5 for training, 6-7 for validation and 8-10 for testing. |
| Hardware Specification | Yes | Our proposed method is implemented and evaluated in Pytorch [15] and we train it on a single RTX 3090 GPU with an AMD EPYC 9654 CPU. |
| Software Dependencies | No | Our proposed method is implemented and evaluated in Pytorch [15] and we train it on a single RTX 3090 GPU with an AMD EPYC 9654 CPU. While Pytorch is mentioned, a specific version number is not provided in the text. |
| Experiment Setup | Yes | The network is trained with Adam optimizer [16] for 40 epochs on 3DMatch and 80 epochs on KITTI with batch size of 1 and weight decay of 10 6. The learning rate initializes from 10 4 and decays exponentially by 0.05 every epoch on 3DMatch and every 4 epochs on KITTI, respectively. |