
## N-Penetrate: Active Learning of Neural Collision Handler for Complex 3D Mesh Deformations
Qingyang Tan, Zherong Pan, Breannan Smith, Takaaki Shiratori, Dinesh Manocha
Keywords: 
ICML/2022/Proceedings/16817 - N-Penetrate: Active Learning of Neural Collision Handler for Complex 3D Mesh Deformations.pdf
Project URL: nan

### Implementation
_Given the documentation given by the authors on the method, how much time investment would it be to re-implement the method from scratch?_

[9]

The authors state implementation details (Framework PyTorch) in section 5. Implementation not given. Overview available in figure 1.

### Data
_Given the data description in the documentation, how much effort take to either: Find the same dataset the authors used, or similar datasets and defend the comparability, or acquire one from scratch?_

[3]

(6/6)

The auhtors state in section 5 they evaluate on 6 datasets, which they briefly summarise with citations and give an overview on in table 1. No direct links.

### Configuration 
_Given the (hyper)parameters, including semantic parameters, of the method: How much effort would it take to acquire the algorithm configurations used for their results, and compare against their budgetary constraints?_

[4]

The hyperparameter settings are summarised in section 5 (informally in text). No acquisition specified.

### Experimental Procedure
_Given the experimental set-up of the work, how difficult is it to set up a new experiment, similar to those presented in the original work, with the same procedure?_

[1]

The authors use accuracy, FNR as metrics. The authors use cross-dataset validation as strategy for table 3. The test set creation is specified in sec 5. 

### Expertise
_How much effort would it take to acquire the expertise required to reproduce the work independently relying on the available documentation?_

[6]

Requires experience with 3D mesh data and robust learning.
