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..
Mono3DVG: 3D Visual Grounding in Monocular Images
Authors: Yang Zhan, Yuan Yuan, Zhitong Xiong
AAAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Comprehensive benchmarks and some insightful analyses are provided for Mono3DVG. Extensive comparisons and ablation studies show that our method significantly outperforms all baselines. |
| Researcher Affiliation | Academia | 1School of Artificial Intelligence, Optics and Electronics (i OPEN), Northwestern Polytechnical University, Xi an, China 2Technical University of Munich (TUM), Munich, Germany |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The dataset and code will be released. |
| Open Datasets | Yes | To facilitate the broad application of 3D visual grounding, we employ both manually annotated and Chat GPT to annotate a large-scale dataset based on KITTI (Geiger, Lenz, and Urtasun 2012) for Mono3DVG. |
| Dataset Splits | Yes | We split our dataset into 29,990, 5,735, and 5,415 expressions for train/val/test sets respectively. |
| Hardware Specification | Yes | We train 60 epochs with a batch size of 10 by Adam W with 10 4 learning rate and 10 4 weight decay on one GTX 3090 24-Gi B GPU. |
| Software Dependencies | No | The paper mentions 'Adam W' as an optimizer but does not provide specific version numbers for any software dependencies like programming languages, libraries, or frameworks. |
| Experiment Setup | Yes | We train 60 epochs with a batch size of 10 by Adam W with 10 4 learning rate and 10 4 weight decay on one GTX 3090 24-Gi B GPU. The learning rate decays by a factor of 10 after 40 epochs. The dropout ratio is set to 0.1. |