DBQ-SSD: Dynamic Ball Query for Efficient 3D Object Detection
Authors: Jinrong Yang, Lin Song, Songtao Liu, Weixin Mao, Zeming Li, Xiaoping Li, Hongbin Sun, Jian Sun, Nanning Zheng
ICLR 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments demonstrate that our method can increase the inference speed by 30%-100% on KITTI, Waymo, and ONCE datasets. |
| Researcher Affiliation | Collaboration | 1Huazhong University of Science and Technology 2Tencent AI Lab 3MEGVII Technology 4Xi an Jiaotong University |
| Pseudocode | No | The paper describes the Dynamic Ball Query (DBQ) network and its inference and training procedures, but it does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper states, "All experiments are implemented by Open PCDet 1 framework. 1https://github.com/open-mmlab/Open PCDet". This link points to a general open-source framework, not the specific code or modifications developed for this paper's methodology. |
| Open Datasets | Yes | We evaluate our detector on two representative datasets: KITTI dataset (Geiger et al., 2012) and Waymo dataset (Sun et al., 2020)... ONCE (Mao et al., 2021b). |
| Dataset Splits | Yes | We randomly sample 16,384 points from the overall point cloud per single view frame... The batch size is set to 16 with 8 GPUs... The initial learning rate is 0.01 and is decayed by 0.1 at 35 and 45 epochs. |
| Hardware Specification | Yes | Latency here is evaluated by a single RTX2080Ti GPU with a batch size of 16. |
| Software Dependencies | No | The paper mentions using "ADAM (Kingma & Ba, 2014) optimizer with onecycle learning strategy (Smith & Topin, 2019)" and that "All experiments are implemented by Open PCDet 1 framework". However, it does not provide specific version numbers for any software libraries or dependencies, such as Python, PyTorch, or Open PCDet itself. |
| Experiment Setup | Yes | We randomly sample 16,384 points from the overall point cloud per single view frame. We train our model by ADAM (Kingma & Ba, 2014) optimizer with onecycle learning strategy (Smith & Topin, 2019). The batch size is set to 16 with 8 GPUs. The initial learning rate is 0.01 and is decayed by 0.1 at 35 and 45 epochs. |