# Sparse-Mono

Monocular Gaussian SLAM predicts a full set of Gaussians for every mapping keyframe, so regions already represented get re-predicted and the map keeps growing. Sparse-Mono encodes the current observation, the persistent map rendered from the current viewpoint, and their photometric and geometric discrepancies, then decides where additions are needed and how many before decoding complete Gaussians only at the selected locations. On Replica it reaches an average PSNR 1.05 dB above MonoGS and uses 89.3% fewer Gaussians than VINGS-Mono, at comparable online processing latency.

Monocular Gaussian SLAM predicts a full set of Gaussians for every mapping keyframe, so regions already represented get re-predicted and the map keeps growing. Sparse-Mono encodes the current observation, the persistent map rendered from the current viewpoint, and their photometric and geometric discrepancies, then decides where additions are needed and how many before decoding complete Gaussians only at the selected locations. On Replica it reaches an average PSNR 1.05 dB above MonoGS and uses 89.3% fewer Gaussians than VINGS-Mono, at comparable online processing latency.