arXiv subjects

Woo Jin Kim

Publications and source records attributed to Woo Jin Kim.

12 recordsLinked to original sources

arXiv2026

Designing heterostructures to control oxygen stoichiometry in helimagnetic perovskite strontium ferrite

A large challenge in determining the physics of helimagnetic SrFeO3 is in stabilizing the stoichiometric chemical phase over long enough time scales to conduct extensive measurements. Degradation in SrFeO3 manifests mainly as a crossover from metallic to insulating behavior. Using a combination of electronic transport and density functional theory, we show that this degradation is dominated by oxygen loss, possibly on the order of one percent. We further demonstrate that high quality SrFeO3 thin films can be stabilized long-term by combining a nanoscale band insulator capping layer with an ex situ ozone anneal. We show that this produces a nearly-pristine cation sublattice and preserves metallicity for at least several weeks. These results establish a reliable pathway for producing chemically stable SrFeO3 thin films, enabling reproducible studies of its unusual helimagnetism.

cond-mat.mtrl-sci↗

arXiv2026

Demo-Pose: Depth-Monocular Modality Fusion For Object Pose Estimation

Object pose estimation is a fundamental task in 3D vision with applications in robotics, AR/VR, and scene understanding. We address the challenge of category-level 9-DoF pose estimation (6D pose + 3Dsize) from RGB-D input, without relying on CAD models during inference. Existing depth-only methods achieve strong results but ignore semantic cues from RGB, while many RGB-D fusion models underperform due to suboptimal cross-modal fusion that fails to align semantic RGB cues with 3D geometric representations. We propose DeMo-Pose, a hybrid architecture that fuses monocular semantic features with depth-based graph convolutional representations via a novel multimodal fusion strategy. To further improve geometric reasoning, we introduce a novel Mesh-Point Loss (MPL) that leverages mesh structure during training without adding inference overhead. Our approach achieves real-time inference and significantly improves over state-of-the-art methods across object categories, outperforming the strong GPV-Pose baseline by 3.2\% on 3D IoU and 11.1\% on pose accuracy on the REAL275 benchmark. The results highlight the effectiveness of depth-RGB fusion and geometry-aware learning, enabling robust category-level 3D pose estimation for real-world applications.

cs.CV↗

arXiv2026

Optical conductivity signatures of strong correlations and multiband superconductivity in infinite-layer nickelates

Since the discovery of superconductivity in infinite-layer nickelates, there have been extensive efforts to unravel their electronic structure and pairing mechanism. In particular, understanding how the electronic structure evolves with doping is essential for clarifying theoretical models of superconductivity in nickelates. Here we present studies of the optical conductivity of Nd1-xSrxNiO2 thin films spanning the full phase diagram 0.025 < x < 0.30 using spectroscopic ellipsometry. The data are consistent with a two-band Drude model, which allows the decomposition of the intraband response into distinct contributions. One is from a "narrow" Drude term which we associate with electron bands, and the other a "broad" Drude term linked to the hole band with strong correlations. Increasing Sr doping leads to an expansion of the hole band spectral weight, and a corresponding reduction in the electron band, indicative of the multiband electronic structure and a doping-dependent reconstruction of the Fermi surface. Both doping and temperature-dependent optical spectra display significant spectral weight transfer from high to low energy, a hallmark of strong electronic correlations. In the superconducting state at optimal doping (x = 0.15), both electron and hole bands contribute to the superconducting condensate, signifying multiband superconductivity.

cond-mat.supr-con↗

arXiv2024

Orbital inversion and emergent lattice dynamics in infinite layer CaCoO$_2$

The layered cobaltate CaCoO$_2$ exhibits a unique herringbone-like structure. Serving as a potential prototype for a new class of complex lattice patterns, we study the properties of CaCoO$_2$ using X-ray absorption spectroscopy (XAS) and resonant inelastic X-ray scattering (RIXS). Our results reveal a significant inter-plane hybridization between the Ca $4s-$ and Co $3d-$orbitals, leading to an inversion of the textbook orbital occupation of a square planar geometry. Further, our RIXS data reveal a strong low energy mode, with anomalous intensity modulations as a function of momentum transfer close to a quasi-static response suggestive of electronic and/or orbital ordering. These findings indicate that the newly discovered herringbone structure exhibited in CaCoO$_2$ may serve as a promising laboratory for the design of materials having strong electronic, orbital and lattice correlations.

cond-mat.str-el↗

arXiv2024

ProDepth: Boosting Self-Supervised Multi-Frame Monocular Depth with Probabilistic Fusion

Self-supervised multi-frame monocular depth estimation relies on the geometric consistency between successive frames under the assumption of a static scene. However, the presence of moving objects in dynamic scenes introduces inevitable inconsistencies, causing misaligned multi-frame feature matching and misleading self-supervision during training. In this paper, we propose a novel framework called ProDepth, which effectively addresses the mismatch problem caused by dynamic objects using a probabilistic approach. We initially deduce the uncertainty associated with static scene assumption by adopting an auxiliary decoder. This decoder analyzes inconsistencies embedded in the cost volume, inferring the probability of areas being dynamic. We then directly rectify the erroneous cost volume for dynamic areas through a Probabilistic Cost Volume Modulation (PCVM) module. Specifically, we derive probability distributions of depth candidates from both single-frame and multi-frame cues, modulating the cost volume by adaptively fusing those distributions based on the inferred uncertainty. Additionally, we present a self-supervision loss reweighting strategy that not only masks out incorrect supervision with high uncertainty but also mitigates the risks in remaining possible dynamic areas in accordance with the probability. Our proposed method excels over state-of-the-art approaches in all metrics on both Cityscapes and KITTI datasets, and demonstrates superior generalization ability on the Waymo Open dataset.

cs.CV↗

arXiv2023

Pixel-Level Equalized Matching for Video Object Segmentation

Feature similarity matching, which transfers the information of the reference frame to the query frame, is a key component in semi-supervised video object segmentation. If surjective matching is adopted, background distractors can easily occur and degrade the performance. Bijective matching mechanisms try to prevent this by restricting the amount of information being transferred to the query frame, but have two limitations: 1) surjective matching cannot be fully leveraged as it is transformed to bijective matching at test time; and 2) test-time manual tuning is required for searching the optimal hyper-parameters. To overcome these limitations while ensuring reliable information transfer, we introduce an equalized matching mechanism. To prevent the reference frame information from being overly referenced, the potential contribution to the query frame is equalized by simply applying a softmax operation along with the query. On public benchmark datasets, our proposed approach achieves a comparable performance to state-of-the-art methods.

cs.CV↗

arXiv2022

Unsupervised Video Anomaly Detection via Normalizing Flows with Implicit Latent Features

In contemporary society, surveillance anomaly detection, i.e., spotting anomalous events such as crimes or accidents in surveillance videos, is a critical task. As anomalies occur rarely, most training data consists of unlabeled videos without anomalous events, which makes the task challenging. Most existing methods use an autoencoder (AE) to learn to reconstruct normal videos; they then detect anomalies based on their failure to reconstruct the appearance of abnormal scenes. However, because anomalies are distinguished by appearance as well as motion, many previous approaches have explicitly separated appearance and motion information-for example, using a pre-trained optical flow model. This explicit separation restricts reciprocal representation capabilities between two types of information. In contrast, we propose an implicit two-path AE (ITAE), a structure in which two encoders implicitly model appearance and motion features, along with a single decoder that combines them to learn normal video patterns. For the complex distribution of normal scenes, we suggest normal density estimation of ITAE features through normalizing flow (NF)-based generative models to learn the tractable likelihoods and identify anomalies using out of distribution detection. NF models intensify ITAE performance by learning normality through implicitly learned features. Finally, we demonstrate the effectiveness of ITAE and its feature distribution modeling on six benchmarks, including databases that contain various anomalies in real-world scenarios.

cs.CV↗

arXiv2022

Character of the "normal state" of the nickelate superconductors

The occurrence of superconductivity in proximity to various strongly correlated phases of matter has drawn extensive focus on their normal state properties, to develop an understanding of the state from which superconductivity emerges. The recent finding of superconductivity in layered nickelates raises similar interests. However, transport measurements of doped infinite-layer nickelate thin films have been hampered by materials limitations of these metastable compounds - in particular, a relatively high density of extended defects. Here, by moving to a substrate (LaAlO$_{3}$)$_{0.3}$(Sr$_{2}$TaAlO$_{6}$)$_{0.7}$ which better stabilizes the growth and reduction conditions, we can synthesize the doping series of Nd$_{1-x}$Sr$_{x}$NiO$_{2}$ essentially free from extended defects. This enables the first examination of the 'intrinsic' temperature and doping dependent evolution of the transport properties. The normal state resistivity exhibits a low-temperature upturn in the underdoped regime, linear behavior near optimal doping, and quadratic temperature dependence for overdoping. This is strikingly similar to the copper oxides, despite key distinctions - namely the absence of an insulating parent compound, multiband electronic structure, and a Mott-Hubbard orbital alignment rather than the charge-transfer insulator of the copper oxides. These results suggest an underlying universality in the emergent electronic properties of both superconducting families.

cond-mat.supr-con↗

arXiv2021

Insulator-to-Metal Crossover near the Edge of the Superconducting Dome in Nd$_{1-x}$Sr$_x$NiO$_2$

We report a systematic magnetotransport study of superconducting infinite-layer nickelate thin films Nd$_{1-x}$Sr$_x$NiO$_2$ with $0.15 \leq x \leq 0.225$. By suppressing superconductivity with out-of-plane magnetic fields up to 37.5 T, we find that the normal state resistivity of Nd$_{1-x}$Sr$_x$NiO$_2$ is characterized by a crossover from a metallic $T^2$-behavior to an insulating log(1/$T$)-behavior for all $x$ except $x = 0.225$, at which the resistivity is predominantly metallic. The log(1/$T$)-behavior is found to be robust against magnetic fields, inconsistent with scenarios involving localization or Kondo scattering, and points to an anomalous insulating state possibly driven by strong correlations. In the metallic state, we find no evidence for non-Fermi-liquid behavior arising from proximity to a putative quantum critical point located inside the superconducting dome.

cond-mat.supr-con↗

arXiv2021

Electronic band structure of (111) $SrRuO_{3}$ thin film$-$an angle-resolved photoemission spectroscopy study

We studied the electronic band structure of pulsed laser deposition (PLD) grown (111)-oriented SrRuO$_3$ (SRO) thin films using \textit{in situ} angle-resolved photoemission spectroscopy (ARPES) technique. We observed previously unreported, light bands with a renormalized quasiparticle effective mass of about 0.8$m_{e}$. The electron-phonon coupling underlying this mass renormalization yields a characteristic "kink" in the band dispersion. The self-energy analysis using the Einstein model suggests five optical phonon modes covering an energy range 44 to 90 meV contribute to the coupling. Besides, we show that the quasiparticle spectral intensity at the Fermi level is considerably suppressed, and two prominent peaks appear in the valance band spectrum at binding energies of 0.8 eV and 1.4 eV, respectively. We discuss the possible implications of these observations. Overall, our work demonstrates that high-quality thin films of oxides with large spin-orbit coupling can be grown along the polar (111) orientation by the PLD technique, enabling \textit{in situ} electronic band structure study. This could allow for characterizing the thickness-dependent evolution of band structure of (111) heterostructures$-$a prerequisite for exploring possible topological quantum states in the bilayer limit.

cond-mat.str-el↗

arXiv2020

AD-VO: Scale-Resilient Visual Odometry Using Attentive Disparity Map

Visual odometry is an essential key for a localization module in SLAM systems. However, previous methods require tuning the system to adapt environment changes. In this paper, we propose a learning-based approach for frame-to-frame monocular visual odometry estimation. The proposed network is only learned by disparity maps for not only covering the environment changes but also solving the scale problem. Furthermore, attention block and skip-ordering scheme are introduced to achieve robust performance in various driving environment. Our network is compared with the conventional methods which use common domain such as color or optical flow. Experimental results confirm that the proposed network shows better performance than other approaches with higher and more stable results.

cs.CV↗

arXiv2018

Unconventional anomalous Hall effect from antiferromagnetic domain walls of Nd2Ir2O7 thin films

Ferroic domain walls (DWs) create different symmetries and ordered states compared with those in single-domain bulk materials. In particular, the DWs of an antiferromagnet (AFM) with non-coplanar spin structure have a distinct symmetry that cannot be realized in those of their ferromagnet counterparts. In this paper, we show that an unconventional anomalous Hall effect (AHE) can arise from the DWs of a non-coplanar AFM, Nd2Ir2O7. Bulk Nd2Ir2O7 has a cubic symmetry; thus, its Hall signal should be zero without an applied magnetic field. The DWs generated in this material break the two-fold rotational symmetry, which allows for finite anomalous Hall conductivity. A strong f-d exchange interaction between the Nd and Ir magnetic moments significantly influences antiferromagnetic domain switching. Our epitaxial Nd2Ir2O7 thin film showed a large enhancement of the AHE signal when the AFM domains switched, indicating that the AHE is mainly due to DWs. Our study highlights the symmetry broken interface of AFM materials as a new means of exploring topological effects and their relevant applications.

cond-mat.str-el↗