# Learning Local Descriptor for Comparing Renders with Real Images
## 5 Conclusions  
We saw that it is possible to learn a feature descriptor to compare simple renders with real images using relatively few image pairs. To do this, we presented a method for producing a data set of patch-pairs, and a method for training the cross-domain descriptor. The learned descriptors showed better performance when larger patches were used, due to simplistic nature of the CADs, i.e., larger patches provided more discriminative context. A two-stage training strategy was necessary for learning the descriptor, where the first stage was for learning initial weights that addressed domain-shift before learning discriminative cross-domain embeddings. The learned descriptors were used for comparing image pairs by nearest neighbor matching and through histograms based on Bag of Visual Words built using the learned features. For nearest-neighbor matching, best FPR95 obtained was 13.8, outperforming SIFT matching for real-real image patches. The learned features can thus be used in a variety of ways, much like other local features. For future work, we note that although we used a basic shading as described before, a slightly more detailed shading, such as that described by [PERSON] [33], might provide better performance while still enabling fast renders. The deep architecture used was VGG16, which is expensive to evaluate and impractical for real-time inspection. Smaller networks such as the one in [10] need to be explored. The hyperparameters for training the network may be chosen using Bayesian methods [34; 35]. The FAST detector was used for sake of dense detections, but its interest points are not best suited for comparing real and synthetic images. Detectors such as LIFT, SIFT, or MSER [5] need to be tested. A detector could also be learned like in LIFT [36]. Instead of matching descriptors one-to-one, matching triplets of descriptors might be an effective alternative, since correspondence between a simplistic CAD and real assembly is not one-to-one. Finally, realistically colored and textured renders with different configurations of the CADs can be explored for augmenting the set of real images. Conceptualization, P.G., I.J., and J.-J.O. ; methodology, P.G. ; software, P.G. and I.J. ; validation, P.G. and I.J. ; investigation, P.G., J.-J.O., and I.J. ; resources, J.-J.O. and I.J. ; data curation, I.J. ; writing--original draft preparation, P.G. ; writing--review and editing, P.G., I.J., and J.-J.O. ; supervision, J.-J.O. and I.J. ; project administration, J.-J.O. ; funding acquisition, J.-J.O. All authors have read and agreed to the published version of the manuscript. This research was funded by IMT Mines Albi and the company Diotasoft. The APC was funded by IMT Mines Albi. Not applicable. All the data used in this work are highly sensitive industrial clients data, hence can not be shared due to their confidential nature. We would like to thank the whole team at Diotasoft Toulouse office, namely, [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON], for their support, guidance, and feedback throughout the span of this work. The authors declare no conflict of interest.