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..
Null-Space Diffusion Sampling for Zero-Shot Point Cloud Completion
Authors: Xinhua Cheng, Nan Zhang, Jiwen Yu, Yinhuai Wang, Ge Li, Jian Zhang
IJCAI 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 5 Experiments; 5.1 Setups Evaluation Metrics. We evaluate the completion performance by computing the Chamfer Distance (CD) between the predicted completion results and the ground truth point clouds following previous methods [Tchapmi et al., 2019; Pan et al., 2021]; 5.2 Point Cloud Completion on Shape Net Datasets. |
| Researcher Affiliation | Academia | Shenzhen Graduate School, Peking University, China EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1 Null-Space Diffusion Sampling. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | Yes | We conduct point cloud completion experiments on three datasets derived from Shape Net [Chang et al., 2015] benchmark with varying forms of degradation. The first dataset is a widely used benchmark PCN [Yuan et al., 2018], which is the subset derived from the Shape Net dataset. The second dataset is provided by Gen Re [Zhang et al., 2018]. |
| Dataset Splits | No | The paper mentions test sets for evaluation but does not provide specific training/validation/test dataset splits for reproduction of the full experimental setup, nor does it explicitly mention a validation set. |
| Hardware Specification | No | The paper does not provide specific hardware details used for running its experiments. |
| Software Dependencies | No | The paper mentions using 'pre-trained Point-Voxel Diffusion (PVD) [Zhou et al., 2021]' but does not provide specific version numbers for PVD or any other software dependencies. |
| Experiment Setup | Yes | For hyper-parameters assigned in the tolerant loop mechanism, we set the loop count N = 20, the loop start time-step L = 100, and the null-space sampling interval K = 10. |