Kaiwei Wang, Yuhan Bao, Shaohua Gao, Wenyong Li
We lifted 2 functions out of this paper's own repositories and ran 2 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| YuHanBaozju/ELP | — | 2 of 2 |
| Function | Status | Where it lives |
|---|---|---|
| adaptive_filter | Ran | YuHanBaozju/ELP/DAVIS_ELP/utils/filter.py pointer only (licence: NONE) · get_code("7ff52ad917ebb46a") |
| update_filter_laplacian_product | Ran | YuHanBaozju/ELP/DAVIS_ELP/utils/filter.py pointer only (licence: NONE) · get_code("df997f1228d3fbbf") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
High-speed autofocus in extreme scenes remains a significant challenge. Traditional methods rely on repeated sampling around the focus position, resulting in "focus hunting". Event-driven methods have advanced focusing speed and improved performance in low-light conditions; however, current approaches still require at least one lengthy round of "focus hunting", involving the collection of a complete focus stack. We introduce the Event Laplacian Product (ELP) focus detection function, which combines event data with grayscale Laplacian information, redefining focus search as a detection task. This innovation enables the first one-step event-driven autofocus, cutting focusing time by up to two-thirds and reducing focusing error by 24 times on the DAVIS346 dataset and 22 times on the EVK4 dataset. Additionally, we present an autofocus pipeline tailored for event-only cameras, achieving accurate results across a range of challenging motion and lighting conditions. All datasets and code are available in https://github.com/YuHanBaozju/ELP.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2503.01214")
get_code_for_paper("2503.01214")
have("2503.01214")
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