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..
Semantic MapNet: Building Allocentric Semantic Maps and Representations from Egocentric Views
Authors: Vincent Cartillier, Zhile Ren, Neha Jain, Stefan Lee, Irfan Essa, Dhruv Batra964-972
AAAI 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | On the task of semantic mapping in the Matterport3D dataset, SMNet significantly outperforms competitive baselines by 4.01 16.81% (absolute) on mean-Io U and 3.81 19.69% (absolute) on Boundary-F1 metrics. Moreover, we show how to use the neural episodic memories and spatio-semantic allocentric representations built by SMNet for subsequent tasks in the same space navigating to objects seen during the tour ( Find chair ) or answering questions about the space ( How many chairs did you see in the house? ). |
| Researcher Affiliation | Collaboration | Vincent Cartillier,1 Zhile Ren,1 Neha Jain,1 Stefan Lee,2 Irfan Essa,1,4 Dhruv Batra,1,3 1 Georgia Institute of Technology 2 Oregon State University 3 Facebook AI Research 4 Google Research EMAIL, EMAIL |
| Pseudocode | No | The paper describes the architecture and modules of SMNet but does not include any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper provides a "Project page: https://vincentcartillier.github.io/smnet.html", which is typically an overview/demo page and not explicitly a source code repository link as required by the prompt. |
| Open Datasets | Yes | We choose the Matterport3D dataset (Chang et al. 2017) with the Habitat simulator (Savva et al. 2019)... Matter Port3D dataset license available at: http://kaldir.vc.in.tum.de/matterport/MP_TOS.pdf. |
| Dataset Splits | Yes | Utilizing the same data split as (Wijmans et al. 2019), we keep 85 unique floors in our dataset: 61 for training, 7 for validation, and 17 for testing. |
| Hardware Specification | Yes | SMNet is trained end-to-end under cross-entropy loss using SGD with learning rate 1e 4, momentum 0.9, weight decay 4e 4, and batch size 8 across 8 Titan XPs. |
| Software Dependencies | No | The paper mentions software like Red Net, Habitat simulator, and SGD but does not provide specific version numbers for any of these or other key software components. |
| Experiment Setup | Yes | SMNet is trained end-to-end under cross-entropy loss using SGD with learning rate 1e 4, momentum 0.9, weight decay 4e 4, and batch size 8 across 8 Titan XPs. Training took 2-3 days. Back propagation is applied after 20 steps. |