UCLID-Net: Single View Reconstruction in Object Space
Authors: Benoit Guillard, Edoardo Remelli, Pascal Fua
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | 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. |