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.
SEFormer: Structure Embedding Transformer for 3D Object Detection
Authors: Xiaoyu Feng, Heming Du, Hehe Fan, Yueqi Duan, Yongpan Liu
AAAI 2023 | Venue PDF | LLM Run Details | Input Tokens: 19,526 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,054 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 show that the proposed architecture can achieve SOTA results on the Waymo Open Dataset, one of the most significant 3D detection benchmarks for autonomous driving. |
| Researcher Affiliation | Academia | 1Tsinghua University, 2Australian National University, 3National University of Singapore |
| Pseudocode | No | The paper does not contain a clearly labeled pseudocode or algorithm block. |
| Open Source Code | Yes | https://github.com/tdzdog/SEFormer. |
| Open Datasets | Yes | The Waymo dataset contains 1000 Li DAR sequences in total. These sequences are further split into 798 training sequences (including around 158k Li DAR samples) and 202 validation sequences (including around 40k Li DAR samples). |
| Dataset Splits | Yes | The Waymo dataset contains 1000 Li DAR sequences in total. These sequences are further split into 798 training sequences (including around 158k Li DAR samples) and 202 validation sequences (including around 40k Li DAR samples). |
| Hardware Specification | Yes | We use 4 RTX 3090 GPUs to train the entire network with batch size 8. |
| Software Dependencies | No | The paper mentions using "Adam W optimizer" and "one-cycle policy" but does not specify software names with version numbers (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | We use 4 RTX 3090 GPUs to train the entire network with batch size 8. We adopt Adam W optimizer and one-cycle policy(Smith and Topin 2019) with division factor 10 and momentum ranges from 0.95 to 0.85 to train the model. The learning rate is initialized with 0.003. The training time is 40 epochs. |