arXiv (All)
open access
When Depth Hurts: Reliability-Aware Geometry Distillation for Depth-Free RGB-D Salient Object Detection
Depth can resolve appearance ambiguity in RGB-D salient object detection (SOD), yet sensor depth is not uniformly reliable. Missing regions, blurred boundaries, and structural artifacts can propagate through multimodal fusion and make an RGB-D detector less accurate than its RGB-only counterpart. Ex
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- S2A:Semantic-to-Spatial Alignment for Alignment-Free RGB-T Salient Object Detection#1063624
- CrossFeat: Bridging Imaging Modalities in Feature Descriptor Space#819729
- CrossFeat: Bridging Imaging Modalities in Feature Descriptor Space#809733
- GaussAnything: Semantic Intent-Driven Refinement of Evolving Gaussian Scenes for Standalone VR#1083353
- OneCanvas: 3D Scene Understanding via Panoramic Reprojection#1118611
Record · ID 926303
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive