A Simple Baseline for Multi-Camera 3D Object Detection
Authors: Yunpeng Zhang, Wenzhao Zheng, Zheng Zhu, Guan Huang, Jiwen Lu, Jie Zhou
AAAI 2023 | Conference PDF | Archive PDF | Plain Text | 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. |