DCDet: Dynamic Cross-based 3D Object Detector
Authors: Shuai Liu, Boyang Li, Zhiyu Fang, Kai Huang
IJCAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments demonstrate the generality and effectiveness of our DCLA and RWIoU-based regression loss. The Code is available at https://github.com/Say2L/DCDet.git. |
| Researcher Affiliation | Academia | School of Computer Science and Engineering, Sun Yat-sen University {liush376@mail2, liby83@mail, fangzhy9@mail2, huangk36@mail}.sysu.edu.cn |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks (clearly labeled algorithm sections or code-like formatted procedures). |
| Open Source Code | Yes | The Code is available at https://github.com/Say2L/DCDet.git. |
| Open Datasets | Yes | In this section, we evaluate models on widely-used 3D object detection benchmark datasets including Waymo Open [Sun et al., 2020] and KITTI [Geiger et al., 2012]. |
| Dataset Splits | Yes | For the Waymo Open dataset, following prior works [Shi et al., 2020a; Wang et al., 2023], models are trained on 20% training samples and evaluated on the whole validation samples. For the KITTI dataset, models are trained on the train set and evaluated on the val set. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. It only mentions general training details without hardware specifics. |
| Software Dependencies | No | The paper mentions using the "Open PCDet [Team, 2020] codebase" and the "Adam optimizer" but does not provide specific version numbers for these or other software dependencies necessary for replication. |
| Experiment Setup | Yes | All models are trained from scratch in an end-to-end manner with the Adam optimizer and a 0.003 learning rate. And the parameter α used in Eq. (4) is set to 0.5. The parameters λcls and λiou used in Eq. (7) are all set to 1. And the parameter λreg used in Eq. (1) and Eq. (7) is set to 3. For the Waymo Open and KITTI datasets, the parameter r used in DCLA is set to 1 and 3, respectively. On the Waymo Open and KITTI datasets, models are trained for 30 epochs with a batch size of 24 and 80 epochs with a batch size of 8, respectively. |