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..
Balanced Chamfer Distance as a Comprehensive Metric for Point Cloud Completion
Authors: Tong Wu, Liang Pan, Junzhe Zhang, Tai WANG, Ziwei Liu, Dahua Lin
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive investigations are provided for the comparison among different metrics and methods for the task of point cloud completion. Experimental results validate that the proposed metric, Density-aware Chamfer Distance, successfully overcomes the aforementioned issues of CD. Our work is implemented with Py Torch and is run on a Tesla V100 GPU. All the models are trained using the Adam optimizer [10] with the learning rate initialized at 1e-4 and decayed by 0.7 every 40 epochs. We use a batch size of 32 and a total epoch of 80. |
| Researcher Affiliation | Collaboration | Tong Wu1, Liang Pan2, Junzhe Zhang2,4, Tai Wang1,3, Ziwei Liu2, Dahua Lin1,3,5 1Sense Time-CUHK Joint Lab, The Chinese University of Hong Kong, 2S-Lab, Nanyang Technological University, 3Shanghai AI Laboratory, 4Sense Time Research, 5Centre of Perceptual and Interactive Intelligence |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | Our code will be available at https://github.com/wutong16/Density_aware_Chamfer_Distance. |
| Open Datasets | Yes | We use the recently proposed MVP Dataset [17] for our study and experiments. |
| Dataset Splits | No | It is a multi-view partial point cloud dataset covering 16 categories with 62,400 and 41,600 pairs for training and testing, respectively. |
| Hardware Specification | Yes | Our work is implemented with Py Torch and is run on a Tesla V100 GPU. |
| Software Dependencies | No | The paper mentions "Py Torch" but does not specify its version number or any other software dependencies with specific version numbers. |
| Experiment Setup | Yes | All the models are trained using the Adam optimizer [10] with the learning rate initialized at 1e-4 and decayed by 0.7 every 40 epochs. We use a batch size of 32 and a total epoch of 80. We set α = 1000 for the evaluation of DCD, and α [40, 100] for training. We set λ [0, 0.5] and β = 9, γ = 1 for our approach in the main experiments. |