Pointmaps to Practice: 3D Multi-view Ocular Lesion Mapping

Uveal melanoma requires volumetric mapping to guide therapy, as two-dimensional frames lack the topology necessary for precise boundary delineation. This work employs DUSt3R-based correspondence estimation with self-calibrated poses and dense pointmaps, refined through intrinsic reprojection loss, while Logarithmic Positional Partition Interval Encoding (LPPIE) is applied to depth data, pointmaps, and camera parameter tuples to reduce memory usage. The pipeline was evaluated on melanoma, nevus, melanosis, pterygium, and phantom ocular images using metrics including completeness (C), mean absolute error (MAE), root mean squared error (RMSE), rotation error, and translation error. For melanoma cases, $C\approx 0.43$ , rotation mismatch $\approx 2^{\circ }$ , RMSE $\approx 0.021$ , and translation error $\approx 3.9$  cm were observed, whereas simpler morphologies achieved C up to 0.61 with stable MAE between 0.005 and 0.008 and $\delta <1.25\approx 0.98$ -0.99. LPPIE substantially reduced the memory footprint, with only minor texture degradation near vascular branching. The method produced coherent meshes under modest computational requirements, though robustness decreased for irregular surfaces, specular reflections, and motion artifacts. The approach operates with single-camera inputs, minimal calibration, and portable hardware, suggesting its potential for accessible ocular volumetry and opportunities for refinement through region constraints, enhanced glare suppression, and precision scheduling.