GIVF: Geometry-Guided Whole-Tree Reconstruction from Crop-Wise Predictions

Whole-tree instance segmentation requires both assigning points to coherent objects and determining which reconstructed instances are valid. This becomes particularly challenging when large forest scenes are processed through overlapping crops, since reconciling local instance masks does not necessarily correct point membership errors within those predictions. We therefore formulate scene-level whole-tree decoding as two related problems: reconstructing point membership and assessing the validity of the resulting instances. To address them, we propose GIVF, a modular framework that integrates geometry-guided instance assembly and learned validity assessment into a single decoding pipeline. GIVF aggregates crop-wise offsets into spatial anchors and combines them with fine-scale geometric tree-membership and trunk-support evidence. Seeded geodesic propagation constructs disjoint whole-tree candidates on a scene-level graph using spatial connectivity and anchor consistency. A lightweight validity model subsequently determines whether each assembled candidate constitutes a valid tree instance, using its geometric structure, vertical profile, trunk support, and native prediction confidence. On four test plots of the Cherlet TLS benchmark, GIVF achieves a macro detection F1 of 0.7254 and a matched-tree mF1 of 0.8865.