A method of recognizing an object, the method comprising: extracting a plurality of feature components of a point cloud of a three-dimensional ( 3 D) image of the object by using position information and colour information of points that form the point cloud; generating a histogram descriptor configured to recognize the object using the extracted feature components; and recognizing the object based on the generated histogram descriptor wherein the histogram descriptor comprises: a first feature component, being a difference of brightness values di i j between a reference point i and a neighbouring point j of the point cloud; a second feature component, being a hue value ah of the neighbouring point j; a third feature component, being an angle θ between a vector b i j from the reference point i to the neighbouring point j and a vector c i j from the reference point i to a projection point j' of the neighbouring point j, wherein the projection point j' is a projection of the neighbouring point j onto the plane that is perpendicular to the normal vector v i at the reference point i wherein the normal vector v i is obtained, with respect to all the points of the point cloud, by using the nearest neighbouring points to reference point i; and a fourth feature component, being an angle φ between a dominant vector m i of the reference point i and the vector c i j from the reference point i to the projection point j' of the neighbouring point j, wherein the fourth feature component is expressed as the following mathematical formula, wherein k is a number of nearest neighbouring points with respect to the reference point i: φ i j = arccos m i ⋅ c i j ‖ m i ‖ ‖ c i j ‖ wherein, m i is equal to ∑ j = 1 k m i j,and m i j is equal to c i j ‖ c i j ‖ di i j × 1 e ‖ c i j ‖ extracting a plurality of feature components of a point cloud of a three-dimensional ( 3 D) image of the object by using position information and colour information of points that form the point cloud; generating a histogram descriptor configured to recognize the object using the extracted feature components; and recognizing the object based on the generated histogram descriptor wherein the histogram descriptor comprises: a first feature component, being a difference of brightness values di i j between a reference point i and a neighbouring point j of the point cloud; a second feature component, being a hue value ah of the neighbouring point j; a third feature component, being an angle θ between a vector b i j from the reference point i to the neighbouring point j and a vector c i j from the reference point i to a projection point j' of the neighbouring point j, wherein the projection point j' is a projection of the neighbouring point j onto the plane that is perpendicular to the normal vector v i at the reference point i wherein the normal vector v i is obtained, with respect to all the points of the point cloud, by using the nearest neighbouring points to reference point i; and a fourth feature component, being an angle φ between a dominant vector m i of the reference point i and the vector c i j from the reference point i to the projection point j' of the neighbouring point j, wherein the fourth feature component is expressed as the following mathematical formula, wherein k is a number of nearest neighbouring points with respect to the reference point i: φ i j = arccos m i ⋅ c i j ‖ m i ‖ ‖ c i j ‖ wherein, m i is equal to ∑ j = 1 k m i j,and m i j is equal to c i j ‖ c i j ‖ di i j × 1 e ‖ c i j ‖ a first feature component, being a difference of brightness values di i j between a reference point i and a neighbouring point j of the point cloud; a second feature component, being a hue value ah of the neighbouring point j; a third feature component, being an angle θ between a vector b i j from the reference point i to the neighbouring point j and a vector c i j from the reference point i to a projection point j' of the neighbouring point j, wherein the projection point j' is a projection of the neighbouring point j onto the plane that is perpendicular to the normal vector v i at the reference point i wherein the normal vector v i is obtained, with respect to all the points of the point cloud, by using the nearest neighbouring points to reference point i; and a fourth feature component, being an angle φ between a dominant vector m i of the reference point i and the vector c i j from the reference point i to the projection point j' of the neighbouring point j, wherein the fourth feature component is expressed as the following mathematical formula, wherein k is a number of nearest neighbouring points with respect to the reference point i: φ i j = arccos m i ⋅ c i j ‖ m i ‖ ‖ c i j ‖ wherein, m i is equal to ∑ j = 1 k m i j,and m i j is equal to c i j ‖ c i j ‖ di i j × 1 e ‖ c i j ‖