Robustifying Generalizable Implicit Shape Networks with a Tunable Non-Parametric Model
Authors: Amine Ouasfi, Adnane Boukhayma
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the improvement obtained through our method with respect to baselines and the state-of-the-art using synthetic and real data. |
| Researcher Affiliation | Academia | Amine Ouasfi Adnane Boukhayma Inria, Univ. Rennes, CNRS, IRISA, M2S, France |
| Pseudocode | Yes | Algorithm 1 The training procedure of our method. |
| Open Source Code | No | The paper does not include an unambiguous statement that the authors are releasing the code for the work described in this paper, nor does it provide a direct link to a source-code repository for their method. |
| Open Datasets | Yes | Shape Net [12] consists of various instances of 13 different synthetic 3D object classes. Scan Net v2[18] is a challenging real dataset containing 1513 room scans as captured with an RBG-D camera. Faust [6] consists of real scans of 10 human body identities in 10 different poses. |
| Dataset Splits | No | The paper mentions using a 'training split' and 'test split' of Shape Net, but does not provide specific percentages, sample counts, or explicit citations to predefined standard splits for reproduction. |
| Hardware Specification | Yes | Our method takes roughly 10 seconds to converge on a NVIDIA RTX A6000 GPU. |
| Software Dependencies | No | The paper mentions using the 'Falcon Library' but does not specify its version number, nor does it list other software dependencies with specific version numbers. |
| Experiment Setup | Yes | We run all the experiments for 100 epochs using Adam optimizer with learning rate 0.1. We experimented with NP = 10k (also 500 and 3k in ablation) and NF = 32 and m = 500 inducing points. |