  Accurate per-object distance estimation is crucial in safety-critical
applications such as autonomous driving, surveillance, and robotics. Existing
approaches rely on two scales: local information (i.e., the bounding box
proportions) or global information, which encodes the semantics of the scene as
well as the spatial relations with neighboring objects. However, these
approaches may struggle with long-range objects and in the presence of strong
occlusions or unusual visual patterns. In this respect, our work aims to
strengthen both local and global cues. Our architecture -- named DistFormer --
builds upon three major components acting jointly: i) a robust context encoder
extracting fine-grained per-object representations; ii) a masked
encoder-decoder module exploiting self-supervision to promote the learning of
useful per-object features; iii) a global refinement module that aggregates
object representations and computes a joint, spatially-consistent estimation.
To evaluate the effectiveness of DistFormer, we conduct experiments on the
standard KITTI dataset and the large-scale NuScenes and MOTSynth datasets. Such
datasets cover various indoor/outdoor environments, changing weather
conditions, appearances, and camera viewpoints. Our comprehensive analysis
shows that DistFormer outperforms existing methods. Moreover, we further delve
into its generalization capabilities, showing its regularization benefits in
zero-shot synth-to-real transfer.
