Individual tree crowns are the primary interface with the environment and closely relate to tree growth, yet accurately characterizing them remains challenging. This study aimed to understand how individual tree stem volume growth (ΔV) depends on crown metrics both at the beginning of the monitoring period (T1_C) and on their changes over time (ΔC), using close-range multisensorial point clouds obtained from terrestrial and airborne laser scanning (TLS and ALS). Data were collected from 22 sample plots in boreal forests of Finland in 2014 (T1) and 2021 (T2). Spearman’s rank correlation coefficient (ρ) was employed to assess the relationships between ΔV and crown metrics across different tree species. Additionally, Random Forest regression (RF) was applied to explore the relative importance of these metrics in explaining ΔV. A strong correlation (ρ = 0.60–0.63) was found between ΔV of Scots pine (Pinus sylvestris L.) and crown metrics, including volume (T1_CV), perimeter (T1_CP), projection area (T1_CA_2D), and top height (T1_CH_max). In contrast, ΔV of Norway spruce (Picea abies (L.) H. Karst.) showed only weak correlations, with the best metrics being crown base height (T1_CH_min), T1_CV, and its change (ΔCV) (ρ = 0.32–0.38). For birches (Betula sp.), ΔV also exhibited weak correlations (ρ = 0.27–0.34), mainly with crown surface area (T1_CA_3D), ΔCV, and T1_CH_max. RF analyses further highlighted species-specific drivers of ΔV. Scots pine with the most important metric of T1_CH_max explained 50% of variation in ΔV. However, ΔCV was the most important metric in explaining ΔV of Norway spruce and birch, with explained variability of 20% and 6%, respectively. In conclusion, this study demonstrated that multisensorial point clouds provide an effective approach to analyze the relationship between ΔV and tree crown structure. Nevertheless, challenges persist in consistently measuring various crown metrics over time and distinguishing actual changes from measurement errors.