Neural radiance field (NeRF) generally requires many images with accurate poses, which do not reflect realistic setups when views can be sparse and poses are imprecise. Previous solutions for sparse and noisy NeRF only consider local geometry consistency with a pair of views. Closely following bundle adjustment in Structure-from-Motion (SfM), we introduce TrackNeRF for more global-consistent geometry reconstruction and more accurate pose optimization. TrackNeRF introduces feature tracks, i.e. connected pixel trajectories across all visible views that correspond to the same 3D points. By enforcing reprojection consistency among feature tracks, TrackNeRF encourages holistic 3D consistency explicitly. Through extensive experiments, TrackNeRF sets a new benchmark in noisy and sparse view reconstruction. In particular, TrackNeRF shows significant improvements over the state-of-the-art BARF and SPARF by 8 and 1 in terms of PSNR on DTU under various sparse and noisy view setups.