ObjCAViT: Improving Monocular Depth Estimation Using Natural Language Models And Image-Object Cross-Attention
This addresses depth estimation ambiguity for computer vision applications, though it appears incremental as it builds on existing transformer-based methods.
The paper tackles monocular depth estimation by incorporating semantic object knowledge and inter-object relationships using language models and cross-attention, achieving competitive results on NYUv2 and KITTI datasets.
While monocular depth estimation (MDE) is an important problem in computer vision, it is difficult due to the ambiguity that results from the compression of a 3D scene into only 2 dimensions. It is common practice in the field to treat it as simple image-to-image translation, without consideration for the semantics of the scene and the objects within it. In contrast, humans and animals have been shown to use higher-level information to solve MDE: prior knowledge of the nature of the objects in the scene, their positions and likely configurations relative to one another, and their apparent sizes have all been shown to help resolve this ambiguity. In this paper, we present a novel method to enhance MDE performance by encouraging use of known-useful information about the semantics of objects and inter-object relationships within a scene. Our novel ObjCAViT module sources world-knowledge from language models and learns inter-object relationships in the context of the MDE problem using transformer attention, incorporating apparent size information. Our method produces highly accurate depth maps, and we obtain competitive results on the NYUv2 and KITTI datasets. Our ablation experiments show that the use of language and cross-attention within the ObjCAViT module increases performance. Code is released at https://github.com/DylanAuty/ObjCAViT.