Three-dimensional　(3D)　reconstruction　of　trees　has　always　been　a　key　task　in　precision　forestry　management　and　research.　Due　to　the　complex　branch　morphological　structure　of　trees　themselves　and　the　occlusions　from　tree　stems,　branches　and　foliage,　it　is　difficult　to　recreate　a　complete　three-dimensional　tree　model　from　a　two-dimensional　image　by　conventional　photogrammetric　methods.　In　this　study,　based　on　tree　images　collected　by　various　cameras　in　different　ways,　the　Neural　Radiance　Fields　(NeRF)　method　was　used　for　individual　tree　dense　reconstruction　and　the　exported　point　cloud　models　are　compared　with　point　clouds　derived　from　photogrammetric　reconstruction　and　laser　scanning　methods.　The　results　show　that　the　NeRF　method　performs　well　in　individual　tree　3D　reconstruction,　as　it　has　a　higher　successful　reconstruction　rate,　better　reconstruction　in　the　canopy　area　and　requires　less　images　as　input.　Compared　with　the　photogrammetric　dense　reconstruction　method,　NeRF　has　significant　advantages　in　reconstruction　efficiency　and　is　adaptable　to　complex　scenes,　but　the　generated　point　cloud　tend　to　be　noisy　and　of　low　resolution.　The　accuracy　of　tree　structural　parameters　(tree　height　and　diameter　at　breast　height)　extracted　from　the　photogrammetric　point　cloud　is　still　higher　than　those　derived　from　the　NeRF　point　cloud.　The　results　of　this　study　illustrate　the　great　potential　of　the　NeRF　method　for　individual　tree　reconstruction,　and　it　provides　new　ideas　and　research　directions　for　3D　reconstruction　and　visualization　of　complex　forest　scenes.