The structure of SE block.

Feature enhancement plays a crucial role in improving the quality and discriminative power of features used in matching tasks. By enhancing the informative and invariant aspects of features, the matching process becomes more robust and reliable, enabling accurate predictions even in challenging scenarios, such as occlusion and reflection in stereo matching. In this paper, we propose an end-to-end dual-dimension feature modulation network called DFMNet to address the issue of mismatches in interference areas. DFMNet utilizes dual-dimension feature modulation (DFM) to capture spatial and channel information separately. This approach enables the adaptive combination of local features with more extensive contextual information, resulting in an enhanced feature representation that is more effective in dealing with challenging scenarios. Additionally, we introduce the concept of cost filter volume (CFV) by utilizing guide weights derived from group-wise correlation. CFV aids in filtering the concatenated volume adaptively, effectively discarding redundant information, and further improving matching accuracy. To enable real-time performance, we designed a fast version named Fast-GFM. Fast-GFM employs the global feature modulation (GFM) block to enhance the feature expression ability, improving the accuracy and stereo matching robustness. The accurate DFMNet and the real-time Fast-GFM achieve state-of-the-art performance across multiple benchmarks, including Scene Flow, KITTI, ETH3D, and Middlebury. These results demonstrate the effectiveness of our proposed methods in enhancing feature representation and significantly improving matching accuracy in various stereo matching scenarios.</div

Dynamics of “Hot” Oxygen Atoms on Ag(100) Surface upon O₂ Dissociation

The dynamics of ballistic adsorbates on metal surfaces are not only important for understanding energy dissipation but also of practical relevance in an array of important applications including corrosion and heterogeneous catalysis. In this work, we examine the early dynamics of “hot” O atoms produced by dissociative chemisorption of O2 on a Ag(100) surface, taking advantage of a high-fidelity machine learned high-dimensional potential energy surface based on first-principles data. Our classical trajectory simulations revealed that the experimentally observed large O–O separations (2–4 nm) can only be reached with hyperthermal incident O2. With thermally impinging O2, the calculated separation between the equilibrated O atoms is about 1 order of magnitude shorter (∼0.3 nm). The relatively low mobility of the “hot” O atoms on this surface is attributed to the fast energy dissipation to surface phonons and a relatively high diffusion barrier. In addition, the O atom diffusion exhibits strong anisotropy dictated by the potential energy surface

Dynamics of “Hot” Oxygen Atoms on Ag(100) Surface upon O₂ Dissociation

The dynamics of ballistic adsorbates on metal surfaces are not only important for understanding energy dissipation but also of practical relevance in an array of important applications including corrosion and heterogeneous catalysis. In this work, we examine the early dynamics of “hot” O atoms produced by dissociative chemisorption of O2 on a Ag(100) surface, taking advantage of a high-fidelity machine learned high-dimensional potential energy surface based on first-principles data. Our classical trajectory simulations revealed that the experimentally observed large O–O separations (2–4 nm) can only be reached with hyperthermal incident O2. With thermally impinging O2, the calculated separation between the equilibrated O atoms is about 1 order of magnitude shorter (∼0.3 nm). The relatively low mobility of the “hot” O atoms on this surface is attributed to the fast energy dissipation to surface phonons and a relatively high diffusion barrier. In addition, the O atom diffusion exhibits strong anisotropy dictated by the potential energy surface

Theoretical Insights into H₂ Activation and Hydrogen Spillover on Near-Surface Alloys with Embedded Single Pt Atoms

Despite extensive studies of hydrogen spillover on single-atom alloy surfaces, a thorough understanding of the structure–activity relationship is still lacking. Here, we investigate H2 dissociation and diffusion of the dissociated H species on the near-surface alloys embedded with single Pt atoms using density functional theory (DFT) calculations and ab initio molecular dynamics (AIMD) simulations. The DFT results indicate that subsurface alloying with early transition metals (X) (Pt1-X/Cu(111)) can generally promote the initial hydrogen spillover but suppress the H2 dissociation process, showing an intractable trade-off effect. While the DFT-calculated H2 dissociation barrier on Pt1-Co/Cu(111) is higher than that on Pt1-Ni/Cu(111), the AIMD results show that the H2 dissociation probability on the Pt1-Co/Cu(111) surface is much higher than that on Pt1-Ni/Cu(111). The trajectory analysis shows that H2 molecules on Pt1-Co/Cu(111) can adopt a more convenient conformation for dissociation when approaching the so-called close-range physisorption zone (CPZ) due to the relatively flat topography of the potential energy surface, thus increasing the H2 dissociation probability compared to the case on Pt1-Ni/Cu(111). This work provides a clear picture for understanding the structure–activity relationships of H2 activation and hydrogen spillover over single-atom catalysts. More importantly, it highlights an overlooked but essential role of the dynamic orientation of the reactant in heterogeneous catalysis

Results on KITTI 2015 [18].

The matching results of interference area marked in red box.</p

Quantitative evaluation on KITTI [17, 18].

Feature enhancement plays a crucial role in improving the quality and discriminative power of features used in matching tasks. By enhancing the informative and invariant aspects of features, the matching process becomes more robust and reliable, enabling accurate predictions even in challenging scenarios, such as occlusion and reflection in stereo matching. In this paper, we propose an end-to-end dual-dimension feature modulation network called DFMNet to address the issue of mismatches in interference areas. DFMNet utilizes dual-dimension feature modulation (DFM) to capture spatial and channel information separately. This approach enables the adaptive combination of local features with more extensive contextual information, resulting in an enhanced feature representation that is more effective in dealing with challenging scenarios. Additionally, we introduce the concept of cost filter volume (CFV) by utilizing guide weights derived from group-wise correlation. CFV aids in filtering the concatenated volume adaptively, effectively discarding redundant information, and further improving matching accuracy. To enable real-time performance, we designed a fast version named Fast-GFM. Fast-GFM employs the global feature modulation (GFM) block to enhance the feature expression ability, improving the accuracy and stereo matching robustness. The accurate DFMNet and the real-time Fast-GFM achieve state-of-the-art performance across multiple benchmarks, including Scene Flow, KITTI, ETH3D, and Middlebury. These results demonstrate the effectiveness of our proposed methods in enhancing feature representation and significantly improving matching accuracy in various stereo matching scenarios.</div

Evaluation results of DFMNet on Scene Flow [19] and ETH3D [33].

Bad1.0 and Bad2.0 represent the proportion of pixels whose prediction differs from the ground truth by more than 1.0 and 2.0, respectively. These metrics are used to evaluate the accuracy of the predictions, and lower values indicate better performance.</p

Dynamics of “Hot” Oxygen Atoms on Ag(100) Surface upon O₂ Dissociation

The dynamics of ballistic adsorbates on metal surfaces are not only important for understanding energy dissipation but also of practical relevance in an array of important applications including corrosion and heterogeneous catalysis. In this work, we examine the early dynamics of “hot” O atoms produced by dissociative chemisorption of O2 on a Ag(100) surface, taking advantage of a high-fidelity machine learned high-dimensional potential energy surface based on first-principles data. Our classical trajectory simulations revealed that the experimentally observed large O–O separations (2–4 nm) can only be reached with hyperthermal incident O2. With thermally impinging O2, the calculated separation between the equilibrated O atoms is about 1 order of magnitude shorter (∼0.3 nm). The relatively low mobility of the “hot” O atoms on this surface is attributed to the fast energy dissipation to surface phonons and a relatively high diffusion barrier. In addition, the O atom diffusion exhibits strong anisotropy dictated by the potential energy surface

Dynamics of “Hot” Oxygen Atoms on Ag(100) Surface upon O₂ Dissociation

The dynamics of ballistic adsorbates on metal surfaces are not only important for understanding energy dissipation but also of practical relevance in an array of important applications including corrosion and heterogeneous catalysis. In this work, we examine the early dynamics of “hot” O atoms produced by dissociative chemisorption of O2 on a Ag(100) surface, taking advantage of a high-fidelity machine learned high-dimensional potential energy surface based on first-principles data. Our classical trajectory simulations revealed that the experimentally observed large O–O separations (2–4 nm) can only be reached with hyperthermal incident O2. With thermally impinging O2, the calculated separation between the equilibrated O atoms is about 1 order of magnitude shorter (∼0.3 nm). The relatively low mobility of the “hot” O atoms on this surface is attributed to the fast energy dissipation to surface phonons and a relatively high diffusion barrier. In addition, the O atom diffusion exhibits strong anisotropy dictated by the potential energy surface

Ablation study of DFMNet on Scene Flow [19].

Feature enhancement plays a crucial role in improving the quality and discriminative power of features used in matching tasks. By enhancing the informative and invariant aspects of features, the matching process becomes more robust and reliable, enabling accurate predictions even in challenging scenarios, such as occlusion and reflection in stereo matching. In this paper, we propose an end-to-end dual-dimension feature modulation network called DFMNet to address the issue of mismatches in interference areas. DFMNet utilizes dual-dimension feature modulation (DFM) to capture spatial and channel information separately. This approach enables the adaptive combination of local features with more extensive contextual information, resulting in an enhanced feature representation that is more effective in dealing with challenging scenarios. Additionally, we introduce the concept of cost filter volume (CFV) by utilizing guide weights derived from group-wise correlation. CFV aids in filtering the concatenated volume adaptively, effectively discarding redundant information, and further improving matching accuracy. To enable real-time performance, we designed a fast version named Fast-GFM. Fast-GFM employs the global feature modulation (GFM) block to enhance the feature expression ability, improving the accuracy and stereo matching robustness. The accurate DFMNet and the real-time Fast-GFM achieve state-of-the-art performance across multiple benchmarks, including Scene Flow, KITTI, ETH3D, and Middlebury. These results demonstrate the effectiveness of our proposed methods in enhancing feature representation and significantly improving matching accuracy in various stereo matching scenarios.</div