Explicit Occlusion Reasoning for 3D Object Detection

Abstract

Consider the problem of recognizing an object that is partially occluded in an image. The visible portions are likely to match learned appearance models for the object, but hidden portions will not. The (hypothetical) ideal system would consider only the visible object information, correctly ignoring all occluded regions. In purely 2D recognition, this requires inferring the occlusion present, which is a significant challenge since the number of possible occlusion masks is, in principle, exponential. We simplify the problem, considering only a small subset of the most likely occlusions (top, bottom, left, and right halves) and noting that some mismatch is tolerable. We train partial-object detectors tailored exactly to each of these few cases. In addition, we reason about objects in 3D and incorporate sensed geometry, as from an RGB-depth camera, along with visual imagery. This allows explicit occlusion masks to be constructed for each object hypothesis. The masks specify how much to trust each partial template, based on their overlap with visible object regions. Only the visible evidence contributes to our object reasoning.