A method for characterizing a scene by computing the 3D orientation of observed elements of the scene, including a step of computing 3D points of the scene (step B1), characterized in that it furthermore includes steps of: - regularly quantifying the 3D points of the scene, along three axes, in a preset 3D grid of voxels (step B2); - for each non-empty voxel of the 3D grid, computing N predefined statistical characteristics from the coordinates of the points contained in the voxel, where N is an integer higher than 1, and for each of the N characteristics, defining a 3D grid associated with said characteristic (step C); - for each 3D grid associated with a statistical characteristic, computing an integral 3D grid associated with said statistical characteristic, each voxel of the integral 3D grid comprising an integral of said statistical characteristic (step D); - for each non-empty voxel of the 3D grid of voxels: ∘ defining a 3D rectangular parallelepipedal vicinity centered on said voxel and of predefined 3D dimensions, and eight vertices of said vicinity; ∘ for each of the N statistical characteristics, computing the integral of the statistical characteristic in said vicinity on the basis of the eight vertices of the integral 3D grid associated with said statistical characteristic; ∘ computing the 3×3 covariance matrix of the 3D points in said vicinity on the basis of said integrals of the statistical characteristics; ∘ computing the orientation of said voxel on the basis of the 3×3 covariance matrix (step E). - regularly quantifying the 3D points of the scene, along three axes, in a preset 3D grid of voxels (step B2); - for each non-empty voxel of the 3D grid, computing N predefined statistical characteristics from the coordinates of the points contained in the voxel, where N is an integer higher than 1, and for each of the N characteristics, defining a 3D grid associated with said characteristic (step C); - for each 3D grid associated with a statistical characteristic, computing an integral 3D grid associated with said statistical characteristic, each voxel of the integral 3D grid comprising an integral of said statistical characteristic (step D); - for each non-empty voxel of the 3D grid of voxels: ∘ defining a 3D rectangular parallelepipedal vicinity centered on said voxel and of predefined 3D dimensions, and eight vertices of said vicinity; ∘ for each of the N statistical characteristics, computing the integral of the statistical characteristic in said vicinity on the basis of the eight vertices of the integral 3D grid associated with said statistical characteristic; ∘ computing the 3×3 covariance matrix of the 3D points in said vicinity on the basis of said integrals of the statistical characteristics; ∘ computing the orientation of said voxel on the basis of the 3×3 covariance matrix (step E). ∘ defining a 3D rectangular parallelepipedal vicinity centered on said voxel and of predefined 3D dimensions, and eight vertices of said vicinity; ∘ for each of the N statistical characteristics, computing the integral of the statistical characteristic in said vicinity on the basis of the eight vertices of the integral 3D grid associated with said statistical characteristic; ∘ computing the 3×3 covariance matrix of the 3D points in said vicinity on the basis of said integrals of the statistical characteristics; ∘ computing the orientation of said voxel on the basis of the 3×3 covariance matrix (step E). <EOS>