# RDVSv2: A Large-scale Benchmark for RGB-D Video Salient Object Detection

arXiv: https://arxiv.org/abs/2607.25392 · HTML: https://mlsanity.com/paper/2607.25392/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 24 of its 46 references that are in the map.
- First posted on arXiv 28 Jul 2026.

## Builds on (most cited first, 24 of 24)

- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [SAM 2: Segment Anything in Images and Videos](https://mlsanity.com/paper/2408.00714/) (2024)
- [RAFT: Recurrent All-Pairs Field Transforms for Optical Flow](https://mlsanity.com/paper/2003.12039/) (2020)
- [Vision Transformers for Dense Prediction](https://mlsanity.com/paper/2103.13413/) (2021)
- [Structure-measure: A New Way to Evaluate Foreground Maps](https://mlsanity.com/paper/1708.00786/) (2017)
- [XMem: Long-Term Video Object Segmentation with an Atkinson-Shiffrin Memory Model](https://mlsanity.com/paper/2207.07115/) (2022)
- [Video Object Segmentation using Space-Time Memory Networks](https://mlsanity.com/paper/1904.00607/) (2019)
- [Salient Object Detection in the Deep Learning Era: An In-Depth Survey](https://mlsanity.com/paper/1904.09146/) (2019)
- [Salient Object Detection: A Benchmark](https://mlsanity.com/paper/1501.02741/) (2015)
- [FlowFormer++: Masked Cost Volume Autoencoding for Pretraining Optical Flow Estimation](https://mlsanity.com/paper/2303.01237/) (2023)
- [JL-DCF: Joint Learning and Densely-Cooperative Fusion Framework for RGB-D Salient Object Detection](https://mlsanity.com/paper/2004.08515/) (2020)
- [Full-Duplex Strategy for Video Object Segmentation](https://mlsanity.com/paper/2108.03151/) (2021)
- [Motion Guided Attention for Video Salient Object Detection](https://mlsanity.com/paper/1909.07061/) (2019)
- [Specificity-preserving RGB-D Saliency Detection](https://mlsanity.com/paper/2108.08162/) (2021)
- [Look Before You Match: Instance Understanding Matters in Video Object Segmentation](https://mlsanity.com/paper/2212.06826/) (2022)
- [Multi-Scale and Detail-Enhanced Segment Anything Model for Salient Object Detection](https://mlsanity.com/paper/2408.04326/) (2024)
- [Point-aware Interaction and CNN-induced Refinement Network for RGB-D Salient Object Detection](https://mlsanity.com/paper/2308.08930/) (2023)
- [A Benchmark Dataset and Saliency-guided Stacked Autoencoders for Video-based Salient Object Detection](https://mlsanity.com/paper/1611.00135/) (2016)
- [Depth-Cooperated Trimodal Network for Video Salient Object Detection](https://mlsanity.com/paper/2202.06060/) (2022)
- [Salient Object Detection in RGB-D Videos](https://mlsanity.com/paper/2310.15482/) (2023)
- [ViDSOD-100: A New Dataset and a Baseline Model for RGB-D Video Salient Object Detection](https://mlsanity.com/paper/2406.12536/) (2024)
- [Adapting Segment Anything Model to Multi-modal Salient Object Detection with Semantic Feature Fusion Guidance](https://mlsanity.com/paper/2408.15063/) (2024)
- [KAN-SAM: Kolmogorov-Arnold Network Guided Segment Anything Model for RGB-T Salient Object Detection](https://mlsanity.com/paper/2504.05878/) (2025)
- [SAM-DAQ: Segment Anything Model with Depth-guided Adaptive Queries for RGB-D Video Salient Object Detection](https://mlsanity.com/paper/2511.09870/) (2025)

## Abstract (arXiv)

We introduce RDVSv2, a large-scale benchmark for RGB-D video salient object detection (RGB-D VSOD) with dense frame-level annotations. Existing datasets in this emerging field are often limited in scale and annotation quality, while also relying on less geometry-consistent depth cues. To address these limitations, RDVSv2 is built from publicly accessible stereoscopic online videos and contains 249 video sequences with 29,077 annotated frames. It includes depth maps derived from stereoscopic videos, together with frame-wise salient object masks annotated with eye-tracking guidance. Compared with existing datasets, RDVSv2 is much larger in scale and covers more diverse and challenging scenarios. In addition, we establish a strong baseline for RGB-D VSOD based on Segment Anything Model 2 (SAM2). Specifically, we employ a parameter-efficient fine-tuning (PEFT) strategy to adapt the SAM2 encoder to jointly encode RGB, depth, and optical flow cues. Extensive experiments show that RDVSv2 is substantially more challenging for existing RGB-D VSOD methods. Meanwhile, the proposed baseline achieves state-of-the-art results on RDVSv2 and existing RGB-D VSOD benchmarks. We hope that RDVSv2 and the provided baseline will serve as useful resources for future research on RGB-D VSOD and related multi-modal video understanding tasks. Our dataset and code will be available at https://github.com/ltynick/RDVSv2.