ArticlePlant methods2026
A three-dimensional reconstruction method for seedlings based on improved DIFIX3D.
Article in Plant methods, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Three-dimensional (3D) reconstruction of seedlings commonly suffers from under-constrained issues in areas with severe occlusion and insufficient observation, leading to false geometry and rendering artifacts. The existing methods primarily rely on pixel-level consistency constraints, resulting in poor stability when reconstructing seedlings with slender branches and complex structures. To address these challenges, this paper proposes a 3D reconstruction method for seedlings which is based on improved DIFIX3D+. By integrating the Stable Diffusion-VAE module into the DIFIX3D+ framework, this approach enhances the reconstruction constraint mechanism at the representation level. Firstly, high-precision segmentation of seedlings was achieved by eliminating background interference using the Florence-2 semantic priors and the SAM2 (Segment Anything Model 2) instance segmentation model. Then, by jointly mapping DIFIX3D+ restored images and their corresponding rendered images into the latent space of Stable Diffusion-VAE, consistency supervision was established within the latent representation domain. It worked in tandem with traditional pixel-level constraints, thereby suppressing the propagation of pixel noise caused by lighting variations, specular reflections, and texture repetitions at the representation level. Experimental results demonstrated that on the self-built dataset, compared to the original 3DGS (3D Gaussian splatting) and DIFIX3D+, the proposed method achieved the peak signal-to-noise ratio (PSNR) improvement of 28.99% and 24.25%, respectively, and the structural similarity index measure (SSIM) improvement of 3.16% and 3.74%, while reducing the learned perceptual image patch similarity (LPIPS) by 9.24% and 41.94%. The average PSNR, SSIM, and LPIPS values reached 31.54 dB, 0.9759, and 0.054, respectively, demonstrating significantly superior reconstruction accuracy compared to mainstream models such as Nerfacto, Plenoxels, and Mip-Splatting. This method combines high visual fidelity with excellent geometric structure representation capabilities, providing a technical reference for high-quality, low-cost 3D reconstruction of seedlings in complex scenes.
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