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..
A Simple Baseline for Multi-Camera 3D Object Detection
Authors: Yunpeng Zhang, Wenzhao Zheng, Zheng Zhu, Guan Huang, Jiwen Lu, Jie Zhou
AAAI 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct extensive experiments on the 3D object detection benchmark of nu Scenes to demonstrate the effectiveness of Sim MOD and achieve competitive performance. |
| Researcher Affiliation | Collaboration | 1Phi Gent Robotics 2Department of Automation, Tsinghua University |
| Pseudocode | No | The paper does not contain any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code will be available at https://github.com/zhangyp15/Sim MOD. |
| Open Datasets | Yes | We conduct extensive experiments on the large-scale autonomous driving dataset nu Scenes (Caesar et al. 2020) to evaluate the proposed method. |
| Dataset Splits | Yes | These sequences are officially split into 700/150/150 ones for training, validation, and testing. |
| Hardware Specification | Yes | We train the model on 8 NVIDIA Ge Force RTX 3090 GPUs with per-GPU batch size as 1. |
| Software Dependencies | No | Sim MOD is implemented based on mmdetection3d. This only mentions one software component and does not provide version numbers for other key dependencies like Python, PyTorch, or CUDA. |
| Experiment Setup | Yes | The model is end-to-end trained with Adam W (Loshchilov and Hutter 2019) optimizer for 24 epochs, with the initial learning rate as 2e-4 and weight decay as 0.01. Multi-step learning rate decay is applied. The input resolution is 1600 900. ... We use the top-scored 600 proposals and filter low-scored proposals. The detection head contains six layers for iterative refinement. |