Object DGCNN: 3D Object Detection using Dynamic Graphs
Authors: Yue Wang, Justin M. Solomon
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our method achieves state-of-the-art performance on autonomous driving benchmarks. We also provide abundant analysis of the detection model and distillation framework. We present our experiments in four parts. We introduce the dataset, metrics, implementation, and optimization details in 6.1. Then, we demonstrate performance on the nu Scenes dataset [66] in 6.2. We present knowledge distillation results in 6.3. Finally, we provide ablation studies in 6.4. |
| Researcher Affiliation | Academia | Yue Wang Massachusetts Institute of Technology yuewang@csail.mit.edu Justin Solomon Massachusetts Institute of Technology jsolomon@mit.edu |
| Pseudocode | No | The paper describes the method procedurally but does not include any labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | We release our code to promote reproducibility and future research. 1https://github.com/WangYueFt/detr3d |
| Open Datasets | Yes | We experiment on the nu Scenes dataset [66]. |
| Dataset Splits | Yes | It has 1K short sequences captured in Boston and Singapore with 700, 150, 150 sequences for training, validation, and testing, respectively. |
| Hardware Specification | Yes | We train for 20 epochs on 8 RTX 3090 GPUs. |
| Software Dependencies | No | The paper mentions 'Adam W' and 'MMDetection3D' but does not specify their version numbers or versions for other key software components. |
| Experiment Setup | Yes | We use Adam W [67] to train the model. The weight decay for Adam W is 10^-2. Following a cyclic schedule [68], the learning rate is initially 10^-4 and gradually increased to 10^-3, which is finally decreased to 10^-8. The model is initialized with a pre-trained Point Pillars network on the same dataset. We train for 20 epochs on 8 RTX 3090 GPUs. |