Abstract: Plants' morphology changes in their growing process. There is a great need of plant's morphology study for future use like biomass estimation, illness and insect infestation, genotype and other agricultural applications. For now, 3D reconstruction methods can get plants' morphology information. It is meaningful to integrate organic matte distribution information in 3D model. NDVI has been proved to an important index in remote sensing and has a close relationship with chlorophyll density. In this work, Tetracam ADC multispectral camera was used. It is a broadband multispectral camera which has 3.2 million pixels in Bayer filter layout on CMOS photosensitive unit. Thirty one multispectral images of a rapeseed plant were collected at three different angles under indoor conditions for 3D reconstruction. The rapeseed plant must remain stationary and background must keep unchanged. A chessboard was added to the scene for control length comparison and to increase background texture detail. The photos equally surrounded rapeseed plant and covered every corner of the scene. Computer vision method, i.e. Structure from motion (SFM) was used to process plant's 3D model. Visual SFM was used for 3D reconstruction and the generated dense point cloud contains 120089 3D points. It worked in the following four steps: 1) extraction of SIFT points, an average of 2490 SIFT points of image were found; 2) motion estimation; 3) bundle adjustment, 3D sparse point cloud contained 3345 points with color were built; 4) dense point cloud generation, point cloud contained 120 089 points with R-G-NIR information were built. The point cloud had a lot of outliers, so a statistical outlier removal method was used for filtering. The removed outliers were 2 682 points. Control length from chess board was used to measure 3D model accuracy. The RMSE of spatial uniformity was 0.052599, and the maximum error was 0.1023 cm. The result showed that this 3D model precisely represented rapeseed plant's morphology. The last step was to extract xyz and r-g-nir data from point cloud, to calculate every point's NDVI and to visualize plants' NDVI spatial distribution. The result generated from Visual SFM was a ply format file which contained 10 fields not only xyz-rgb. So six fields of x-y-z-r-g-nir were extracted from original data and NDVI index of every point was calculated. The histogram of rape 3D model's NDVI showed the amount of point distributed on every NDVI value. As the NDVI value of background chessboard paper and desktop were below 0, their NDVI were set 0. To visualize the NDVI spatial distribution, a pseudo-color transform was performed according to color transformation theory. After pseudo color transformation, NDVI values were transformed into RGB color and the result ply file containing six field x-y-z-r-g-b. The results showed that the attempts to integrate multispectral image information into plant 3D reconstruction worked out well and had a potential for plants' organic matters spatial distribution research. Compare to other 3D reconstruction method like structure-light 3D reconstruction and laser scanning, SFM had less limitations including no need for special instrument and accessory; good reconstruction result; and being able to integrate NIR information in the point cloud. In the future, this method can be used for insects and illness positioning, plant stress reaction and some similar study.