  Image-based 3D shape retrieval (IBSR) aims to find the corresponding 3D shape
of a given 2D image from a large 3D shape database. The common routine is to
map 2D images and 3D shapes into an embedding space and define (or learn) a
shape similarity measure. While metric learning with some adaptation techniques
seems to be a natural solution to shape similarity learning, the performance is
often unsatisfactory for fine-grained shape retrieval. In the paper, we
identify the source of the poor performance and propose a practical solution to
this problem. We find that the shape difference between a negative pair is
entangled with the texture gap, making metric learning ineffective in pushing
away negative pairs. To tackle this issue, we develop a geometry-focused
multi-view metric learning framework empowered by texture synthesis. The
synthesis of textures for 3D shape models creates hard triplets, which suppress
the adverse effects of rich texture in 2D images, thereby push the network to
focus more on discovering geometric characteristics. Our approach shows
state-of-the-art performance on a recently released large-scale 3D-FUTURE[1]
repository, as well as three widely studied benchmarks, including Pix3D[2],
Stanford Cars[3], and Comp Cars[4]. Codes will be made publicly available at:
https://github.com/3D-FRONT-FUTURE/IBSR-texture
