Kick Back & Relax++: Scaling Beyond Ground-Truth Depth with SlowTV & CribsTV

arXiv:2403.01569v1 Announce Type: new
Abstract: Self-supervised learning is the key to unlocking generic computer vision systems. By eliminating the reliance on ground-truth annotations, it allows scaling to much larger data quantities. Unfortunately, self-supervised monocular depth estimation (SS-MDE) has been limited by the absence of diverse training data. Existing datasets have focused exclusively on urban driving in densely populated cities, resulting in models that fail to generalize beyond this domain.
To address these limitations, this paper proposes two novel datasets: SlowTV …

abstract annotations arxiv beyond computer computer vision cs.ai cs.cv cs.ro data diverse ground-truth key kick mde reliance scaling self-supervised learning supervised learning systems the key training training data truth type vision