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..
3D Shape Completion with Multi-View Consistent Inference
Authors: Tao Hu, Zhizhong Han, Matthias Zwicker10997-11004
AAAI 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results demonstrate that our method completes shapes more accurately than previous techniques. |
| Researcher Affiliation | Academia | Tao Hu, Zhizhong Han, Matthias Zwicker Department of Computer Science University of Maryland, College Park EMAIL, EMAIL |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement or link regarding the public availability of its source code. |
| Open Datasets | Yes | We take 3D models from Shape Net (Chang et al. 2015). |
| Dataset Splits | No | The paper mentions using a 'training dataset' and a 'test dataset' from previous works but does not explicitly provide details about training, validation, and test splits (e.g., percentages, counts, or explicit validation set mention). |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper mentions various techniques and frameworks like U-Net, GAN, and batch normalization, but does not provide specific version numbers for any software dependencies or libraries. |
| Experiment Setup | Yes | We optimize the shape descriptor z for 100 gradient descent steps, and we take z with the smallest consistency loss in the last 10 steps as z . It should be mentioned that since the gradients of z are small, we use a large learning rate of 0.2. ... According to the comparison, we select L2 distance to calculate generator loss, and set μ = 1 in the following experiments. ... The average CD is lower when we increase the size of the depth-buffer, as shown in Table 2 (a)... Given a depth-buffer size of 5 × 5, Fig. 6 (e) to (g) show that the consistency loss increases when J = 3 or J = 5... |