To address the reliance of high-precision camera-line-laser joint calibration on complex highaccuracy targets in industrial 3D color reconstruction, this paper proposes a high-precision calibration scheme based on a multi-feature, weakly constrained calibration block. The overall method comprises a multimodal feature extraction and registration framework, together with a two-stage optimization-based solver for the calibration model. Circular-hole centers are introduced on the calibration block and jointly detected with corner points. Corners are localized with sub-pixel accuracy using geometric constraints, while circular-hole centers are precisely estimated via a two-stage ellipse fitting strategy. Subsequently, a pose-adaptive projection-based method for 3D feature reconstruction is presented Following a dimensionality-reduction-detection-lifting pipeline, the 3D localization problem is transformed into 2D feature detection and then back-projected to reconstruct the 3D point cloud, thereby improving robustness to noise and pose variations. Finally, unambiguous 2D-3D feature point registration is achieved by incorporating geometric priors. For parameter estimation, a linear decomposition-nonlinear reconstruction two-stage optimization is adopted. The initial mapping matrix is linearly estimated from single-frame feature correspondences; after separating intrinsic and extrinsic parameters via RQ decomposition, lens distortion is incorporated for global nonlinear refinement to enhance global optimality and generalization. Experimental results indicates that, the proposed method achieves a normalized mean reprojection error of 0.84 pixels, corresponding to a physical distance error of 0.019 4 mm. Compared with the baseline method, those two error metrics are reduced by approximately 65% and 61%, respectively. The proposed method also yields consistent calibration results with small error fluctuations under three illumination conditions, indicating strong robustness. Ablation results further confirm that center features are significantly more stable than corner features under perspective transformation. In the gear tooth-surface color reconstruction task, point-cloud color texture mapping based on the obtained mapping matrix faithfully reproduces microscopic impressions and scratches on the tooth surface, thereby validating its engineering applicability.