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 [1].
Accurate Point Cloud Registration with Robust Optimal Transport
Authors: Zhengyang Shen, Jean Feydy, Peirong Liu, Ariel H Curiale, Ruben San Jose Estepar, Raul San Jose Estepar, Marc Niethammer
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our proposed models achieve SOTA results for scene flow on Kitti [78, 77] and for point-cloud-based lung registration on Dir Lab-COPDGene [19]. Notably, we show that Rob OT is highly suited to fine-tuning tasks: it consistently turns good matchings into nearly perfect registrations at an affordable numerical cost. |
| Researcher Affiliation | Collaboration | Zhengyang Shen UNC Chapel Hill EMAIL Jean Feydy Imperial College London EMAIL Peirong Liu UNC Chapel Hill EMAIL Ariel Hernán Curiale Harvard Medical School EMAIL Ruben San José Estépar Harvard Medical School EMAIL Raúl San José Estépar Harvard Medical School EMAIL Marc Niethammer UNC Chapel Hill EMAIL |
| Pseudocode | No | The paper describes algorithms and methods in detail using equations and textual explanations but does not provide structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our code and dataset are available online at: https://github.com/uncbiag/robot. |
| Open Datasets | Yes | We train on the synthetic Flying3D dataset [75], which is made up of multiple moving objects that are sampled at random from Shape Net. |
| Dataset Splits | Yes | For all of our experiments, we randomly sample 600 training and 100 validation cases from this large collection of unannotated patients. |
| Hardware Specification | Yes | As detailed in Suppl. A.6, all run times were measured on a single GPU (24GB NVIDIA Quadro RTX 6000). |
| Software Dependencies | Yes | We then combine Eq. (3) with Eq. (4) to compute the Rob OT vectors vi and weights wi with O(N + M) memory footprint using the Ke Ops library [20, 37] for Py Torch [86] and Num Py [117]. |
| Experiment Setup | Yes | We follow the same experimental setting as in [126, 49], with full details provided in Suppl. A.4.3: 1. We train on the synthetic Flying3D dataset [75], which is made up of multiple moving objects that are sampled at random from Shape Net. We take 19,640 pairs of point clouds for training, with dense ground truth correspondences. 2. We evaluate on 142 scene pairs from Kitti, a real-world dataset [78, 77]. We conduct experiments using 8,192 and 30k points per scan, sampled at random from the original data. |