CAGroup3D: Class-Aware Grouping for 3D Object Detection on Point Clouds
Authors: Haiyang Wang, Lihe Ding, Shaocong Dong, Shaoshuai Shi, Aoxue Li, Jianan Li, Zhenguo Li, Liwei Wang
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our model achieves state-of-the-art 3D detection performance with remarkable gains of +3.6% on Scan Net V2 and +2.6% on SUN RGB-D in term of m AP@0.25. and We conduct extensive ablation studys on the val sets of Scan Net V2 to analyze individual components of our proposed method. |
| Researcher Affiliation | Collaboration | 1Center for Data Science, Peking University 2Beijing Institute of Technology 3Max Planck Institute for Informatics 4Huawei Noah s Ark Lab, China 5National Key Laboratory of General Artificial Intelligence, School of Intelligence Science and Technology, Peking University |
| Pseudocode | No | The paper describes methods in prose and figures, but does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | Code will be available at https://github.com/Haiyang-W/CAGroup3D. |
| Open Datasets | Yes | Our CAGroup3D is evaluated on two indoor challenging 3D scene datasets, i.e., Scan Net V2 [6] and SUN RGB-D [33]. |
| Dataset Splits | Yes | For all datasets, we follow the standard data splits adopted in [27]. Scan Net V2 contains richly-annotated 3D reconstructed indoor scenes with axis-aligned bounding box for most common 18 object categories. It contains 1201 training samples and the remaining 312 scans are left for validation. |
| Hardware Specification | Yes | All models are trained on two NVIDIA Tesla V100 GPUs with a 32 GB memory per-card. |
| Software Dependencies | No | The paper mentions 'Adam W optimizer [23]' and 'Mind Spore*' but does not provide specific version numbers for these or other software dependencies. |
| Experiment Setup | Yes | For both datasets, we set the voxel size as 0.02m. ... Training Scan Net V2 requires 120 epochs with the learning rate decay by 10x on 80 epochs and 110 epochs. SUN RGB-D takes 48 epochs and learning rate decayed on 32, 44 epochs. ... we set batch size, initial learning rate and weight decay are 16, 0.001 and 0.0001 for both datasets. |