Automated object protrusion loss adjustment

A method comprising:

• receiving a dataset comprising a plurality of voxels associated with an environment and loss values for individual voxels of the plurality of voxels;
• determining a loss for an individual occupied voxel of the plurality of voxels;
• determining an occupancy status for a subset of the plurality of voxels proximate to the individual occupied voxel;
• determining, based at least in part on the occupancy status for individual voxels of the subset of the plurality of voxels, a density of proximate occupied voxels;
• determining, based at least in part on the density of the proximate occupied voxels and the loss, an adjusted loss for the individual occupied voxel;
• and
• training a machine-learned (ML) object detection model based at least in part on the adjusted loss.

One or more non-transitory computer-readable media storing instructions that, when executed by one or more processors, perform operations comprising:

• receiving a dataset comprising a plurality of data units associated with an environment;
• determining a loss for an individual data unit of the plurality of data units;
• determining an occupancy status for a subset of the plurality of data units proximate to the individual data unit;
• determining, based at least in part on the occupancy status for individual data units of the subset of the plurality of data units, a density of proximate occupied data units;
• determining, based at least in part on the density of the proximate occupied data units and the loss, an adjusted loss for the individual data unit;
• and
• training a machine-learned (ML) object detection model based at least in part on the adjusted loss.