EndoGeoNet: Geometry-Arbitrated Thin-Branch Segmentation for Forest Navigation

Thin branches are difficult to segment in forest scenes because they occupy few pixels and often share appearance with surrounding vegetation. We introduce EndoGeoNet, a monocular segmentation framework that augments a strong RGB backbone with geometry estimated from the same input image. A frozen monocular depth estimator provides a relative-depth cue, which is encoded by a lightweight multiscale geometry branch and fused at four decoder stages through Geo-Visual Arbitration Modules (GVAMs). Each GVAM learns stage-specific appearance–geometry weights, allowing the model to suppress unreliable depth while retaining useful geometric corrections. We also introduce the Forestry Sparse-Branch Dataset (FSB), a dedicated benchmark of 60 manually annotated full-HD images acquired across different locations and capture batches, together with topology- and component-aware evaluation. On FSB, EndoGeoNet improves the RGB baseline from 0.587 to 0.629 Dice and from 0.537 to 0.580 clDice, while maintaining near-identical performance on DIS-5K (0.899 versus 0.900 Dice). Replacing aligned depth with zero or shuffled depth reduces Dice by 2.19 and 2.63 points, respectively, showing that the model relies on spatially corresponding geometry. Including online depth estimation, the full pipeline runs at 7.74 FPS in FP32 and 16.74 FPS with mixed precision on an RTX 4090.