Neural　Radiance　Fields　(NeRF)　has　recently　overhauled　novel　view　synthesis,　but　it　requires　extensive　computations　for　training　and　captures　variations　in　detail　with　difficulty.　In　this　paper,　we　propose　a　novel　framework,　termed　CD-TDRF,　to　mitigate　these　dilemmas.　CD-TDRF　factorizes　a　density　voxel　grid　into　a　core　tensor　and　three　matrices　via　Tucker　decomposition,　reducing　memory　usage　and　accelerating　training.　To　better　capture　variations　in　complex　scenes,　CD-TDRF　uses　a　fully　convolutional　network　to　extract　prior　information　from　the　training　images.　Moreover,　three　learnable　appearance　planes　are　constructed　to　preserve　information　about　scene　details,　which　enhances　the　rendering　quality　significantly.　Our　experimental　results　demonstrate　that　CD-TDRF　has　achieved　competitive　rendering　quality　on　three　popular　datasets　and　speeds　up　training　compared　with　traditional　NeRF　models.