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..
UCLID-Net: Single View Reconstruction in Object Space
Authors: Benoit Guillard, Edoardo Remelli, Pascal Fua
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate both on Shape Net synthetic images, which are often used for benchmarking purposes, and on real-world images that our approach outperforms state-of-the-art ones. |
| Researcher Affiliation | Academia | Benoit Guillard Edoardo Remelli CVLab EPFL, Switzerland {firstname.lastname}@epfl.ch |
| Pseudocode | No | The paper describes the architecture and processes through textual descriptions and diagrams but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code is available here: https://github.com/cvlab-epfl/UCLID-Net. |
| Open Datasets | Yes | Shape Net Core [2] features 38000 shapes belonging 13 object categories... PIX3D [19] is a collection of pairs of real images of furniture with ground truth 3D models and pose annotations. |
| Dataset Splits | Yes | We use the same testing and training splits but re-generated the depth maps because the provided ones are clipped along the z-axis. We therefore use it for validation only, on approximately 2.5k images of chairs. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running the experiments (e.g., GPU/CPU models, memory). |
| Software Dependencies | No | The paper mentions 'implemented in Pytorch' but does not provide specific version numbers for PyTorch or any other software dependencies. |
| Experiment Setup | Yes | The network is implemented in Pytorch, and trained for 150 epochs using the Adam optimizer, with initial learning rate 10 3, decreased to 10 4 after 100 epochs. |