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..
EmbodiedSAM: Online Segment Any 3D Thing in Real Time
Authors: Xiuwei Xu, Huangxing Chen, Linqing Zhao, Ziwei Wang, Jie Zhou, Jiwen Lu
ICLR 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on Scan Net, Scan Net200, Scene NN and 3RScan show our method achieves state-of-the-art performance among online 3D perception models, even outperforming offline VFM-assisted 3D instance segmentation methods by a large margin. |
| Researcher Affiliation | Academia | 1Tsinghua University, 2Nanyang Technological University |
| Pseudocode | No | The paper describes methods with textual explanations and mathematical formulas (Eq 1-9) but does not include any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code is available1. 1Project page: https://xuxw98.github.io/ESAM/ |
| Open Datasets | Yes | We evaluate our method on four datasets: Scan Net Dai et al. (2017), Scan Net200 Rozenberszki et al. (2022), Scene NN Hua et al. (2016) and 3RScan Wald et al. (2019). |
| Dataset Splits | Yes | Scan Net contains 1513 scanned scenes, out of which we use 1201 sequences for training and the rest 312 for testing. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory amounts) used for running its experiments. It mentions 'VFM' and '3D U-Net' but not the underlying hardware. |
| Software Dependencies | No | The paper does not provide specific ancillary software details, such as library names with version numbers (e.g., PyTorch 1.9, CUDA 11.1). |
| Experiment Setup | Yes | For hyperparameters, we set ϕ = 0.5, ϵ = 1.75, τ = 0.02, α = 0.5 and β = 0.5. In the dual-level query decoder, we actually set F = FS for the first two iterations of mask prediction, and then set F = FP . |