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.
DV-3DLane: End-to-end Multi-modal 3D Lane Detection with Dual-view Representation
Authors: Yueru Luo, Shuguang Cui, Zhen Li
ICLR 2024 | Venue PDF | LLM Run Details | Input Tokens: 22,009 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,869 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 on the public benchmark, Open Lane, demonstrate the efficacy and efficiency of DV-3DLane. It achieves state-of-the-art performance. with a remarkable 11.2 gain in F1 score and a substantial 53.5% reduction in errors. |
| Researcher Affiliation | Academia | 1 FNii, CUHK-Shenzhen 2 School of Science and Engineering, CUHK-Shenzhen {222010057@link.,shuguangcui@,lizhen@}cuhk.edu.cn |
| Pseudocode | Yes | Algorithm 1 Bidirectional Feature Fusion (BFF) Input: Li DAR points Ppt, image I, camera parameters T Output: mm-aware PV features Fpv, BEV features Fbev, mm denotes multi-modal. |
| Open Source Code | Yes | The code is available at https://github.com/JMoonr/dv-3dlane. |
| Open Datasets | Yes | We evaluate our method on Open Lane Chen et al. (2022), the sole public 3D lane dataset featuring multi-modal sources, Open Lane is a large-scale dataset built on Waymo Open Dataset Sun et al. (2020) |
| Dataset Splits | No | The paper mentions training and testing on datasets like Open Lane-1K and Open Lane-300, but it does not specify the exact percentages or sample counts for validation splits, nor does it explicitly cite predefined validation splits within the paper. |
| Hardware Specification | Yes | All models are tested on a single V100 GPU |
| Software Dependencies | No | The paper mentions using 'Adam optimizer' and 'cosine annealing scheduler' but does not specify software versions for these or other dependencies like Python, PyTorch, or CUDA. |
| Experiment Setup | Yes | We set the number of lane queries to 40, and we employ deformable attention with 4 heads, 8 sample points, and 256 embedding dimensions. We use the Adam optimizer Kingma & Ba (2014) with a weight decay of 0.01. The learning rate is set to 2e-4, and our models undergo training for 24 epochs with a batch size of 32. We employ the cosine annealing scheduler Loshchilov & Hutter (2016) with Tmax = 8. Our input images are of resolution 720 960, and we adopt a voxel size of (0.2m, 0.4m) for the X and Y axes. |