To tackle this issue, we conducted the theoretical analysis to promote the effectiveness of contrast decoding.

Consequently, it is uncertain whether the generated modalities sufficiently represent the diversity of future behaviors.

As large language models gain widespread adoption, running them efficiently becomesacrucialtask.

Results for 3DMatch are shown in Table 1: the performance gain achieved by Compass when deploying theproposed data augmentation validates itsimportance. Indeed, without theproposed augmentation FLARE performs better than Compass on this dataset. This dataset has been specifically proposed to verify the invariance to rotations of the learned 3D descriptors [1], and containsonlyatestsplit. In Figure 2, we consider two pairs of local surface patches and their corresponding feature maps: both patches forming a pair are extracted around the same keypoint on different fragments. The canonical pose computed for the first pair is repeatable, while the second pair represents a failure ofCompass.

This task is commonly addressed by handcrafted algorithms exploiting geometric cues deemed as distinctive and robust by the designer. Yet, one might conjecture that humans learn the notion oftheinherent orientation of3Dobjectsfromexperience andthatmachines may do so alike. In this work, we show the feasibility of learning a robust canonical orientation for surfaces represented as point clouds.

To tackle this issue, we conducted the theoretical analysis to promote the effectiveness of contrast decoding.

Consequently, it is uncertain whether the generated modalities sufficiently represent the diversity of future behaviors.