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..
SimDistill: Simulated Multi-Modal Distillation for BEV 3D Object Detection
Authors: Haimei Zhao, Qiming Zhang, Shanshan Zhao, Zhe Chen, Jing Zhang, Dacheng Tao
AAAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments validate the effectiveness and superiority of Sim Distill over state-of-the-art methods, achieving an improvement of 4.8% m AP and 4.1% NDS over the baseline detector. The source code will be released at https://github.com/Vi TAE-Transformer/Sim Distill. |
| Researcher Affiliation | Academia | 1School of Computer Science, The University of Sydney, Australia, 2School of Computing, Engineering and Mathematical Sciences, La Trobe University, Australia |
| Pseudocode | No | The paper does not contain any pseudocode or algorithm blocks. |
| Open Source Code | Yes | The source code will be released at https://github.com/Vi TAE-Transformer/Sim Distill. |
| Open Datasets | Yes | We follow the common practice (Huang et al. 2021; Liu et al. 2023; Liang et al. 2022; Li et al. 2023b; Chen et al. 2023) to evaluate our method on the most challenging benchmark, i.e., nu Scenes (Caesar et al. 2020). |
| Dataset Splits | Yes | It comprises 700 scenes for training, 150 scenes for validation, and 150 scenes for testing. |
| Hardware Specification | Yes | Our method is implemented with Py Torch using 8 NVIDIA A100 (40G Memory), based on the MMDetection3D codebase (Contributors 2020). |
| Software Dependencies | No | The paper mentions "Py Torch" and "MMDetection3D codebase" but does not specify their version numbers. |
| Experiment Setup | Yes | train the student model for 20 epochs with batch size 24. |