Multi-Horizon Feature Reconstruction for 3D Salient Object Detection

Surya Kant Singh; Vivek Kumar Srivastav; Rajeev Srivastava

doi:10.2478/ijssis-2026-0065

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Multi-Horizon Feature Reconstruction for 3D Salient Object Detection

International Journal on Smart Sensing and Intelligent Systems

Volume 19 (2026): Issue 1 (January 2026)

By: Surya Kant Singh, Vivek Kumar Srivastav and Rajeev Srivastava

Open Access

|Sep 2026

Figures & Tables

The demonstration of multi-horizon-attention and feature optimization using muti-scaled, multi-resolution, multi-stage color, depth, and common modality-based features. **(A)** Image. **(B)** Depth. **(C)** Ground-truth. **(D)** Our.

The left part shows the architecture of the proposed model. First, the model extracts features from the RGB image and depth map. While extracting the complementary features, the RGB FAO. Then these cross-model features are fused in a MFR. Finally, the outputs of these MFR modules are fed to a SAM in four steps to obtain the final saliency map. FAO, feature aggregation and optimization; MFR, multi-horizon feature reconstruction; SAM, self-aggregation module.

Stage-wise optimization of extracted features has visualized the reconstruction of visual salient points in the low-depth images to predict the salient objects accurately.

Table 1:

The quantitative comparison of the proposed model on six benchmark RGBD datasets with four recent evaluation parameters.

Date-Set	Metric	OUR	Improving [53]	CATNet [54]	FCN [37]	AirSod [25]	MAD [55]	DCMF [56]	GCFNET [22]	HIFN [50]	CAS-GNN [29]	cmMS [33]	CoNet [51]	D3Net [20]	CPFP [15]	TANet [57]	PCFNET [52]	CTMF [27]	AFNet [26]	DF [13]
NJUD [6]	Fβ↑	0.945	0.940	0.929	0.918	0.918	0.903	0.888	0.915	0.915	0.893	0.914	0.872	0.900	0.877	0.874	0.872	0.845	0.775	0.804
NJUD [6]	Sα↑	0.941	0.926	0.937	0.918	0.908	0.921	0.913	0.914	0.912	0.911	0.900	0.894	0.900	0.879	0.878	0.877	0.849	0.772	0.763
Eψ↑	0.969	0.959	0.933	0.953	0.944	0.930	0.925	0.947	0.944	0.922	0.914	0.912	0.950	0.926	0.925	0.924	0.913	0.853	0.864
MAE↓	0.024	0.026	0.025	0.039	0.034	0.037	0.043	0.038	0.038	0.036	0.044	0.047	0.041	0.053	0.060	0.059	0.085	0.100	0.141
NLPR [46]	Fβ↑	0.928	0.934	0.916	0.911	0.923	0.901	0.875	0.907	0.915	0.888	0.913	0.850	0.897	0.867	0.863	0.841	0.825	0.771	0.778
NLPR [46]	Sα↑	0.949	0.932	0.939	0.924	0.908	0.933	0.922	0.919	0.924	0.919	0.899	0.907	0.912	0.888	0.886	0.874	0.860	0.799	0.802
Eψ↑	0.966	0.966	0.968	0.960	0.963	0.955	0.940	0.953	0.960	0.951	0.945	0.936	0.953	0.932	0.941	0.925	0.929	0.879	0.880
MAE↓	0.019	0.019	0.018	0.024	0.039	0.022	0.029	0.025	0.924	0.025	0.027	0.031	0.030	0.036	0.041	0.044	0.056	0.058	0.085
STEREO [47]	Fβ↑	0.945	0.933	0.902	0.906	0.900	0.892	0.872	0.900	0.906	0.876	0.908	0.885	0.891	0.871	0.861	0.860	0.831	0.823	0.757
STEREO [47]	Sα↑	0.933	0.922	0.925	0.906	0.895	0.910	0.903	0.899	0.907	0.899	0.889	0.908	0.899	0.879	0.874	0.875	0.848	0.825	0.757
Eψ↑	0.967	0.953	0.935	0.947	0.939	0.939	0.930	0.940	0.944	0.929	0.922	0.922	0.938	0.925	0.923	0.925	0.912	0.887	0.847
MAE↓	0.031	0.026	0.030	0.032	0.043	0.037	0.043	0.040	0.040	0.039	0.042	0.041	0.046	0.051	0.060	0.064	0.086	0.075	0.141
SSD [48]	Fβ↑	0.889	0.861	-	-	-	0.850	-	-	0.870	0.840	0.865	0.806	0.834	0.766	0.810	0.807	0.729	0.687	0.735
SSD [48]	Sα↑	0.887	0.871	-	-	-	0.872	-	-	0.875	0.872	0.874	0.853	0.857	0.807	0.839	0.841	0.776	0.711	0.747
Eψ↑	0.940	0.917	-	-	-	0.907	-	-	0.917	0.915	0.911	0.896	0.910	0.852	0.897	0.894	0.865	0.807	0.828
MAE↓	0.042	0.045	-	-	-	0.045	-	-	0.045	0.047	0.052	0.059	0.058	0.082	0.063	0.062	0.099	0.118	0.142
LFSD [49]	Fβ↑	0.919	0.857	0.884	0.876	-	0.862	0.771	0.771	0.849	0.832	0.888	0.848	0.810	0.826	0.796	0.775	0.787	0.744	0.813
LFSD [49]	Sα↑	0.909	0.821	0.894	0.875	-	0.867	0.879	0.846	0.845	0.846	0.846	0.862	0.825	0.828	0.801	0.786	0.788	0.738	0.783
Eψ↑	0.930	0.849	0.908	0.913	-	0.901	0.842	0.883	0.907	0.877	0.891	0.897	0.862	0.872	0.847	0.827	0.857	0.815	0.857
MAE↓	0.041	0.096	0.051	0.061	-	0.059	0.068	0.079	0.070	0.074	0.72	0.071	0.095	0.088	0.111	0.119	0.127	0.133	0.145
DUT-RGBD [5]	Fβ↑	0.960	0.859	0.951	0.931	0.920	-	0.928	0.923	0.913	0.912	0.932	0.908	0.867	0.795	0.790	0.771	0.823	0.659	0.744
DUT-RGBD [5]	Sα↑	0.957	0.841	0.953	0.924	0.891	-	0.920	0.918	0.902	0.891	0.885	0.918	0.882	0.818	0.808	0.801	0.831	0.762	0.705
Eψ↑	0.977	0.879	0.971	0.956	0.946	-	0.950	0.951	0.929	0.932	0.940	0.941	0.889	0.859	0.861	0.856	0.899	0.796	0.823
MAE↓	0.020	0.071	0.020	0.032	0.048	-	0.043	0.040	0.041	0.043	0.036	0.034	0.061	0.076	0.093	0.100	0.097	0.122	0.145

[i] MAE, mean absolute error.

Table 2:

Illustration of the contribution of optimization of each component of the proposed model.

Setting					STEREO [47]				NJU2K [6]				LFSD [49]
BASE MODEL	Low-depthissues	OUR	MFR	SAM	*F_β↑*	*S_α↑*	*E_ψ↑*	*MAE↓*	*F_β↑*	*S_α↑*	*E_ψ↑*	*MAE↓*	*F_β↑*	*S_α↑*	*E_ψ↑*	*MAE↓*
✓					0.8038	0.8064	0.8572	0.0894	0.7559	0.7766	0.7703	0.1003	0.7095	0.7105	0.7090	0.1963
✓	✓				0.8152	0.8334	0.8442	0.0840	0.7889	0.8690	0.8088	0.0900	0.7245	0.7505	0.7590	0.1083
✓	✓	✓			0.8481	0.8452	0.8555	0.0752	0.8245	0.8489	0.8505	0.0762	0.8335	0.8452	0.8580	0.0882
✓	✓	✓	✓		0.8582	0.8609	0.8781	0.0651	0.8772	0.8778	0.8780	0.0610	0.8676	0.8590	0.8775	0.0790
✓	✓	✓	✓	✓	0.9456	0.9334	0.9674	0.0310	0.9456	0.9414	0.9698	0.0245	0.9197	0.9097	0.9370	0.0411

[i] MAE, mean absolute error; MFR, multi-horizon feature reconstruction; SAM, self-aggregation module.

The saliency maps is shown here for the visual demonstration **(A)** input RGB image, **(B)** depth image, **(C)** GT image, **(D)** OUR, **(E)** HAIN, **(F)** CAS−GNN, **(G)** cmMS, **(H)** CoNet, **(I)** D3NET, **(J)** CPSF, **(K)** TANet, **(L)** PCFNET, **(M)** CTMFR, and **(N)** AFNet of proposed model. GT, ground truth.

Comparison of our proposed model with recent models with **(A)** PR-curve **(B)** F-measure (vertical axis (thresholds) and horizontal axis) datasets (1) STERE (2) LFSD dataset, respectively.

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DOI: https://doi.org/10.2478/ijssis-2026-0065 | Journal eISSN: 1178-5608

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Language: English

Submitted on: May 19, 2026

Published on: Sep 4, 2026

Published by: International Journal on Smart Sensing and Intelligent Systems

In partnership with: Paradigm Publishing Services

Publication frequency: Volume open

Keywords:

RGBD,

complex salient object detection,

convolutional neural network,

backbone network,

multi-horizon reconstruction module

Related subjects:

Engineering,

Introductions and overviews,

Engineering, other

© 2026 Surya Kant Singh, Vivek Kumar Srivastav, Rajeev Srivastava, published by International Journal on Smart Sensing and Intelligent Systems
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Volume 19 (2026): Issue 1 (January 2026)

Multi-Horizon Feature Reconstruction for 3D Salient Object Detection

Figures & Tables

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Table 1:

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