Perception

Depth Completion

Depth Completion is the task of producing a dense depth map from sparse or incomplete depth measurements, usually guided by an aligned RGB image. Sparsity arises from LiDAR's limited scan lines, stereo failures on textureless regions, and depth-camera dropouts on reflective or transparent surfaces. Modern methods use convolutional or transformer networks that fuse image features with the valid depth samples to inpaint missing regions.

Why it matters for physical AI

Grasp planners and collision checkers fail on holes in depth data, which are common precisely on the shiny and transparent objects robots must handle. Completion recovers usable geometry from imperfect, low-cost sensors.