The competitive stress of individual trees can be quantified by considering their positions and dimensions such as diameter at breast height (dbh) and height with respect to their neighbor trees. However, measurements of these attributes in the field limit the number of trees and stands that can be assessed with given resources. In recent years, terrestrial laser scanning (TLS) and airborne laser scanning (ALS) data have become prominent in characterizing three-dimensional forest structures. These data could also provide efficient and reliable tools to assess the competitive stress of trees within a stand. Therefore, we aimed to investigate the capability of TLS and low-altitude ALS in characterizing the competitive stress affecting individual trees in boreal forests. We compared: a) an object-based approach that quantified competition through the identification and characterization of competing neighbor trees from the TLS and ALS point clouds, and b) a point cloud-based approach where the presence of point cloud structures representing competitive vegetation around a target tree was considered. Accordingly, three object-based competition indices (CIs) utilizing dbh (CIdbh), height (CIH), and maximum crown diameter (CIMCD) as weights were calculated using the Hegyi equation. For the point cloud-based approach, the canopy density index (CDI), and the competitive pressure index (CPI) were derived using an upside-down search cone set at 60 % relative tree height, while the CICylinder was calculated by counting the number of voxels occupied by the competitive vegetation inside a fixed-radius cylinder. These laser scanning-based CIs were assessed against in situ-based CIs where dbh and height were used as weights in the Hegyi equation. The results showed that the object-based CIs were more correlated (r = 0.33–0.48, p-value < 0.001) with the in situ-based CIs in comparison with the point cloud-based CIs (r = −0.22–0.37). The object-based CIs showed a high correlation (r = 0.65–0.71, p-value < 0.001) when compared between TLS and ALS, while a greater variation was observed for the point cloud-based CIs (r = 0.29–0.53, p-value < 0.001). Tree detection rate and the number of neighboring trees in the field affected how well the CIs derived from the TLS and ALS data were in line with the in situ-based CIs, especially when the competitive stress was assessed using the object-based CIs. In conclusion, the object-based CIs derived using TLS and ALS provided consistent characterization of competition in managed boreal forests compared to the in situ-based CIs. While TLS is ideal for small-scale assessments, low-altitude ALS offers a rather similar capacity for assessing competition but with broader coverage. In complex forest structures, reliable tree detection is essential to avoid underestimating the competitive stress of trees.