Notice: 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. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
CRAFT: Camera-Radar 3D Object Detection with Spatio-Contextual Fusion Transformer
Authors: Youngseok Kim, Sanmin Kim, Jun Won Choi, Dongsuk Kum
AAAI 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our camera-radar fusion approach achieves the state-of-the-art 41.1% m AP and 52.3% NDS on the nu Scenes test set, which is 8.7 and 10.8 points higher than the cameraonly baseline, as well as yielding competitive performance on the Li DAR method. ... 4 Experiments ... 4.1 Comparison with State-of-the-Arts ... 4.2 Ablation Studies ... 4.3 Analysis |
| Researcher Affiliation | Academia | Youngseok Kim1, Sanmin Kim1, Jun Won Choi2, Dongsuk Kum1 1Korea Advanced Institute of Science and Technology 2Hanyang University EMAIL, EMAIL |
| Pseudocode | No | The paper describes algorithms and methods in text and figures but does not include any explicit pseudocode blocks or algorithm listings. |
| Open Source Code | No | The paper does not provide any explicit statements about open-sourcing the code or a link to a code repository. |
| Open Datasets | Yes | We evaluate our method on a large-scale and challenging nu Scenes dataset (Caesar et al. 2020). |
| Dataset Splits | Yes | which consists of 700/150/150 scenes for train/val/test set. |
| Hardware Specification | Yes | We train our models for 24 epochs with a batch size of 32, cosine annealing scheduler, and 2 × 10−4 learning rate on 4 RTX 3090 GPUs. Inference time is measured on an Intel Core i9 CPU and an RTX 3090 GPU without test time augmentation for fusion. |
| Software Dependencies | No | The paper mentions using CenterNet and DLA34 backbone, but it does not specify version numbers for these or other software libraries/frameworks. |
| Experiment Setup | Yes | We train our models for 24 epochs with a batch size of 32, cosine annealing scheduler, and 2 × 10−4 learning rate on 4 RTX 3090 GPUs. |