VisNet: Projective Geometry-View Fusion for Point Cloud Visibility

Per-point visibility prediction from raw point clouds requires reasoning about both 3D geometry and view-dependent occlusion. Existing learning-based approaches primarily infer visibility from geometric representations conditioned on viewing direction, without explicitly modeling occlusion interactions between points that become locally coupled after projection into the image plane. We introduce projective geometry-view fusion, which combines 3D geometric context with explicit occlusion reasoning over projected point neighborhoods. VisNet implements this idea by constructing a view-dependent graph over projected neighborhoods and reasoning about possible occlusion through local message passing. The resulting view-specific representations are then fused with 3D geometric features through cross-cue interactions and neighborhood-level information exchange on the same projected graph. VisNet achieves state-of-the-art performance on sparse, noisy, and out-of-distribution point clouds, improving accuracy by 1.4-3.4% and mIoU by 3.0-6.9% over prior methods across 1k-32k inputs.