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The measurement and analysis of fruit phenotypes are crucial aspects of plant breeding and genetics research. Methods for phenotype detection using single-view RGBD images offer high throughput and low cost but are limited by sensor resolution and perspective, often failing to obtain data such as the surface area and volume of fruits. An improved method was proposed based on PFNET that used a depth camera to capture single-view point clouds of spherical-like fruits for high-precision 3D reconstruction and non-invasive phenotype measurements. To address the issue of varying input scales in the completion network, an adaptive geometric completion strategy was introduced to transform single-view point clouds into approximate hemispheres. The addition of a fourth scale to the PFNET framework enhanced the utilization of dense point clouds acquired by KINECT cameras, facilitating the completion of complex shapes and structures rich in detail. By incorporating a four head self-attention module, the network’s ability to capture interdependencies and spatial relationships among points in the point cloud was improved, enhancing feature extraction capabilities. An optimized fruit point cloud module was added to resolve issues with local diffusion in the original network generated point clouds and to improve their quality. A targeted phenotype detection method, designed to mimic manual measurements, was also proposed. Experimental results showed that this method achieved point cloud quality comparable to that obtained by structured light 3D scanners for citrus fruits, with high fidelity in 3D reconstruction. For the detection of four phenotypes—transverse diameter, longitudinal diameter, surface area, and volume—the R2 values exceeded 0.96, with average measurement accuracy above 93.24%, approaching that of 3D scanners but at 50 times of the efficiency and one-tenth of the cost. Compared with RGBD image methods, the single fruit detection time was increased by 17.97 s, but there was a significant improvement in transverse and longitudinal diameter detection accuracy, allowing for the simultaneous measurement of four phenotypic parameters. Compared with the 3D scanner method, the difference in detection accuracy was within 4 percentage points, but the speed was more than 48 times faster, with hardware costs reduced to only one-tenth of the latter’s and easier implementation of automation. This method struck a good balance between detection accuracy, operational speed, hardware cost, and level of automation, offering a cost-effective, high-performance 3D reconstruction technology with great potential for non-invasive measurement of spherical-like fruit phenotypes.