  Multi-view counting (MVC) methods have shown their superiority over
single-view counterparts, particularly in situations characterized by heavy
occlusion and severe perspective distortions. However, hand-crafted heuristic
features and identical camera layout requirements in conventional MVC methods
limit their applicability and scalability in real-world scenarios.In this work,
we propose a concise 3D MVC framework called \textbf{CountFormer}to elevate
multi-view image-level features to a scene-level volume representation and
estimate the 3D density map based on the volume features. By incorporating a
camera encoding strategy, CountFormer successfully embeds camera parameters
into the volume query and image-level features, enabling it to handle various
camera layouts with significant differences.Furthermore, we introduce a feature
lifting module capitalized on the attention mechanism to transform image-level
features into a 3D volume representation for each camera view. Subsequently,
the multi-view volume aggregation module attentively aggregates various
multi-view volumes to create a comprehensive scene-level volume representation,
allowing CountFormer to handle images captured by arbitrary dynamic camera
layouts. The proposed method performs favorably against the state-of-the-art
approaches across various widely used datasets, demonstrating its greater
suitability for real-world deployment compared to conventional MVC frameworks.
