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..
Unsupervised Feature Learning for 3D Scene Reconstruction with Occupancy Maps
Authors: Vitor Guizilini, Fabio Ramos
AAAI 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments conducted in simulated and real large-scale datasets show a substantial gain in performance, while decreasing the amount of stored information by orders of magnitude without sacrificing accuracy. |
| Researcher Affiliation | Academia | Vitor Guizilini, Fabio Ramos School of Information Technologies, University of Sydney EMAIL |
| Pseudocode | Yes | Algorithm 1 ASK-Means initialization algorithm |
| Open Source Code | Yes | 1A C++ demo of the proposed algorithm is available in https: //bitbucket.org/vguizilini/cvpp |
| Open Datasets | Yes | In this section we validate the proposed algorithm using three different datasets: Room, a simulated indoor environment; Corridor, a real indoor environment; and Outdoor, a real outdoor environment2. ... 2Both real datasets were obtained from http://kos.informatik. uni-osnabrueck.de/3Dscans/ |
| Dataset Splits | No | The paper mentions 'different ratios of training/testing points' and 'every training/testing ratio' but does not provide specific percentages or counts for a fixed training/validation/test split. |
| Hardware Specification | No | The paper does not provide any specific hardware details such as GPU or CPU models used for running the experiments. |
| Software Dependencies | No | The paper mentions various algorithms and methods (e.g., K-means, Hilbert Maps, kd-tree) and cites their original papers, but it does not specify any software dependencies with version numbers (e.g., programming languages, libraries, or frameworks with their versions) used for implementation or experimentation. |
| Experiment Setup | Yes | In all experiments, ASK-means (Sec. ) was used with a threshold of 0.1% the maximum distance between points. ... Unoccupied points are generated by randomly sampling the beams that produced occupied points (a ratio of 1 point / 2 meters was used throughout the paper). |