Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
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 | Venue PDF | LLM Run Details | Input Tokens: 23,406 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 5,695 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| 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. |