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..
APR: Online Distant Point Cloud Registration through Aggregated Point Cloud Reconstruction
Authors: Quan Liu, Yunsong Zhou, Hongzi Zhu, Shan Chang, Minyi Guo
IJCAI 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct extensive experiments against state-of-the-art (SOTA) feature extractors on KITTI and nu Scenes datasets. Results show that APR outperforms all other extractors by a large margin, increasing average registration recall of SOTA extractors by 7.1% on Lo KITTI and 4.6% on Lo Nu Scenes. |
| Researcher Affiliation | Academia | 1Shanghai Jiao Tong University 2Donghua University EMAIL, EMAIL |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code is available at https://github.com/liu Quan98/APR. |
| Open Datasets | Yes | We conduct extensive experiments against state-of-the-art (SOTA) feature extractors on KITTI and nu Scenes datasets. Previously, only close-range registration datasets have been extracted from KITTI [Geiger et al., 2012] and nu Scenes [Caesar et al., 2020]. For alignment of nonkey frames in APG, we use only ground-truth pose from Semantic KITTI [Jens et al., 2019] and nu Scenes [Caesar et al., 2020]. |
| Dataset Splits | Yes | We distill two low-overlap point cloud datasets, i.e., Lo KITTI and Lo Nu Scenes, with 30% overlap from KITTI and nu Scenes and conduct extensive experiments. Table 2 lists the performance of FCGF+APR(a/s) and Predator+APR(a) on KITTI[5, 20] validation set. As a result, we first pre-train a model on a dataset with lower distance, where d [5, 20]. Then the pre-trained model is further finetuned on [5, d2] (d2 30) to guarantee convergence. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. |
| Experiment Setup | Yes | We set default parameters as ψ = 3 and α = 10. Specifically, the asymmetrical decoder is an MLP with flexible hidden layer size, e.g., (2^9, 2^8) reveals a 3-layer MLP with l, 512, 256, ϕ 3 dimensions from input to output. The decoder with the size of (2^9, 2^8) can achieve the best RR performance. As a result, we first pre-train a model on a dataset with lower distance, where d [5, 20]. Then the pre-trained model is further finetuned on [5, d2] (d2 30) to guarantee convergence. |