Active 3D Shape Reconstruction from Vision and Touch
Authors: Edward Smith, David Meger, Luis Pineda, Roberto Calandra, Jitendra Malik, Adriana Romero Soriano, Michal Drozdzal
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments show the benefits of such solutions in the task of 3D shape understanding where our models consistently outperform natural baselines. We provide our framework as a tool to foster future research in this direction. |
| Researcher Affiliation | Collaboration | Edward J. Smith1,2 David Meger2 Luis Pineda1 Roberto Calandra1 Jitendra Malik1,3 Adriana Romero-Soriano1,2, Michal Drozdzal1, 1 Facebook AI Research 2 Mc Gill University 3 University of California, Berkeley |
| Pseudocode | No | The paper describes the steps of the simulator and the reconstruction pipeline conceptually (e.g., Figure 1, Figure 3, detailed paragraphs) but does not include any formal pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our framework, training and evaluation setup, and trained models are publicly available on a Git Hub repository to ensure and encourage reproducible experimental comparison 3. 3https://github.com/facebookresearch/Active-3D-Vision-and-Touch |
| Open Datasets | Yes | The dataset used is made up of 40,000 objects sampled from the ABC dataset [34, 56], a CAD model dataset of approximately one million objects. |
| Dataset Splits | Yes | This set of objects was split into 5 sets; 3 training sets 5 of size 7,700 object each, a validation set comprised 2,000 objects, and a test set of size 1,000. |
| Hardware Specification | Yes | All steps in this procedure are performed in parallel or using GPU accelerated computing, and as a result across the 50 grasping options of 100 randomly chosen objects, simulated grasps and touch signals are produced in 0.0317 seconds each on a Tesla V100 GPU with 16 CPU cores. |
| Software Dependencies | No | In our simulator, all steps are performed in python across the robotics simulator Py Bullet [15], the rendering tool Pyrender [39], and Py Torch [48]. The paper lists software but does not specify version numbers for reproducibility. |
| Experiment Setup | No | The paper mentions aspects of the experimental setup such as number of grasps (5 grasps) and performing hyper-parameter search, but it does not provide specific numerical values for hyperparameters like learning rates, batch sizes, or optimizer settings in the main text. |