The 3D convolution would return an output volume, but you could try to reduce one of the dimensions (e.g. the depth).
I.e. an input of [batch_size, channels, depth, height, width] would result in an output of [batch_size, out_channels, depth*, height*, width*], where the * shapes are calculated depending on the kernel size, stride, dilation etc.