arXiv Computer Vision

When Depth Hurts: Reliability-Aware Geometry Distillation for Depth-Free RGB-D Salient Object Detection

arXiv:2609. 03378v1 Announce Type: new Abstract: Depth can resolve appearance ambiguity in RGB-D salient object detection (SOD), yet sensor depth is not uniformly reliable.

arXiv Computer Vision
Sep 14

RA-SOD: Reliability-Aware RGB-T Salient Object Detection under Modality Degradation

RA‑SOD is a new RGB‑Thermal salient object detection framework that explicitly models the reliability of each modality. It introduces a reliability‑conditioned representation, an uncertainty‑guided dual‑stream refinement, and a pixel‑wise modality competition mechanism to adaptively compensate degraded features and suppress unreliable evidence. Experiments on four benchmarks show that RA‑SOD achieves state‑of‑the‑art performance and remains robust under severe modality degradation.

By Hongbo Gao, Zhengyu Li, Xueru Nie, Dihao Zhu, Lijun Zhao, Yunke Wang, Chang Xu
arXiv Computer Vision
Sep 23

DIFTA-3D: Depth-Consistent Instance-Level Feature Transfer and Adaptation of DINOv3 for 3D Detection

The paper introduces DIFTA-3D, a method that replaces the task‑specific visual branch in IIFNet3D with a frozen DINOv3 foundation model for RGB‑D 3D instance detection. It employs a depth‑consistent feature pipeline that projects points into calibrated RGB‑D frames, filters features with a metric depth‑residual check, caches accepted DINOv3 features, and aggregates them within proposal‑aligned RoI grids. Extensive experiments on ScanNetV2 show that the DINOv3 control achieves mAP scores of 76.15/60.93 at IoU thresholds 0.25/0.50, while the Conservative VAID recipe improves these to 76.59/62.16, indicating a modest gain from the proposed transfer recipe.

By Linman Wang, ZiFei Zhang, Chunran Zheng, Xiwang Dong, Jiarong Lin
arXiv AI
Oct 9

Distilling Routed 3D Privilege for Spatial Reasoning in Vision-Language Models

The paper introduces Geometry-Privileged Distillation (GPD), a method that incorporates depth, semantic, and bird’s-eye-view cues as compact text routed to a teacher model during on-policy self-distillation. By applying a privileged KL loss only to incorrect trajectories, GPD enhances spatial reasoning in vision‑language models while keeping the deployed model RGB‑only. Experiments on a 4B backbone show that GPD outperforms prior methods on multiple spatial reasoning benchmarks, with ablations confirming the benefits of 3D cues, question‑conditioned routing, and selective distillation.

By Hongxing Li, Yixin Li, Dingming Li, Zixuan Wang, Yuchen Yan, Wenqi Zhang, Weiming Lu, Yongliang Shen
arXiv Computer Vision
Sep 25

RGBD20K: A Large-Scale Benchmark for RGB-D Semantic Segmentation

RGBD20K is a new large-scale RGB‑D semantic segmentation dataset featuring 20,000 image pairs and 160 fine‑grained categories, surpassing existing benchmarks like NYUv2 and SUN RGB‑D in both scale and semantic diversity. The dataset provides high‑fidelity annotations obtained through rigorous re‑evaluation and correction of prior labels, ensuring a clean ground‑truth foundation. Additionally, the authors introduce a score‑purified fusion (SPF) method that achieves state‑of‑the‑art performance across evaluated benchmarks, demonstrating the value of high‑quality multimodal information.

By Shaohua Dong, Zexuan Meng, Haiyan Sun, Bing Fan, Cuicui Zhang, Dylan Joseph, Kewei Sha, Yunhe Feng, Heng Fan