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..
3D Crowd Counting via Multi-View Fusion with 3D Gaussian Kernels
Authors: Qi Zhang, Antoni B. Chan12837-12844
AAAI 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The proposed method is tested on 3 multi-view counting datasets and achieves better or comparable counting performance to the state-of-the-art. |
| Researcher Affiliation | Academia | Qi Zhang, Antoni B. Chan Department of Computer Science, City University of Hong Kong Tat Chee 83, Kowloon Tong, Hong Kong SAR, China EMAIL, EMAIL |
| Pseudocode | No | The paper describes the system architecture and process flow but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide an explicit statement about releasing code or a direct link to a code repository for the described methodology. |
| Open Datasets | Yes | The proposed method is evaluated and compared on the 3 multi-view counting datasets, PETS2009 (Ferryman and Shahrokni 2009), Duke MTMC (Ristani, Solera, and et al. 2016) and City Street (Zhang and Chan 2019). |
| Dataset Splits | No | The paper specifies training and testing splits for datasets but does not explicitly define a separate validation dataset split. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running its experiments, such as GPU/CPU models or memory amounts. |
| Software Dependencies | No | The paper does not provide specific details about ancillary software dependencies, such as library names with version numbers. |
| Experiment Setup | Yes | In the first stage, β = 1 means that the single-view 2D supervision is dominant to benefit the feature extraction training; In the second stage, β = 0.01 means the 3D supervision is dominant to accelerate the 3D fusion training; In the third stage, β = 0.01 and γ is variable according to ground-truth setting, which increases the influence of the projection consistency measure (PCM) to further enhance the performance. In all stages, the learning rate is set as 10 4 and the batch size is 1. |