TL;DR
The Depth Segment Anything Model (DepthSAM), a MDE-adapted method specifically designed to mitigate this misalignment, achieves both robust semantic understanding in camouflaged environments and accurate segmentation of camouflaged objects.
A mask-aware tri-modal framework that improves the quality of superpoint representations by retrieving a scene-level structural context from a pretrained PointSAM encoder to enhance object-centric evidence and predicting a soft mask weight to suppress unreliable superpoints.
SphereSOD is proposed, an ERP-native framework that couples panoramic geometry with evolving salient structures and first initializes deformable sampling based on spherical projection geometry and then employs bounded, content-adaptive offsets, yielding features that are better aligned with the underlying panoramic geo...
This work enhances the existing iterative object-basesd visual localization approach with an additional semantic feature derived from a pretrained semantic segmentation model and conducts a systematic baseline study of contemporary feature matching techniques on such cross-domain query-reference image pairs.
Marigold V2 achieves state-of-the-art results when applied to other dense regression tasks, such as surface normals estimation and intrinsic image decomposition, and achieves state-of-the-art results when applied to other dense regression tasks, such as surface normals estimation and intrinsic image decomposition.
This work proposes three learned matching heads: a LightGlue-style attention head with DoubleSoftmax scoring on frozen MASt3R descriptors; a DPT-style multi-scale fusion module that exposes layered spatial detail from the VGGT foundation model before pooling; and a multi-view extension that performs joint self-attentio...
A depth uncertainty-guided feature modulation method is proposed, in which depth entropy and variance are jointly modeled to generate a BEV alignment confidence map, enabling adaptive enhancement and suppression of image features and effectively mitigating cross-modal alignment errors.
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