arXiv subjects

Muxin Liu

Publications and source records attributed to Muxin Liu.

11 recordsLinked to original sources

arXiv2026

Monocular Depth Estimation from a Single Image: Progress and Opportunities

Monocular depth estimation has long stood as a fundamental challenge in computer vision, enabling a wide range of applications including 3D reconstruction, robotics, autonomous driving, and augmented reality. This survey traces the field's evolution from early learning-based methods to the emergence of transformative foundation models. We begin by framing the problem, distinguishing between relative and metric depth estimation, and highlighting the key challenges that have shaped a decade of research. We then present common problem formulations and introduce the most widely used datasets, covering indoor, outdoor, and synthetic data. Following this, we review major advances prior to the foundation model era, distilling core insights from influential methods that contributed to improvements in accuracy, efficiency, and robustness. The survey then turns to the recent surge of foundation-model-based approaches, categorizing them into discriminative and generative paradigms and emphasizing the critical roles of large-scale pretraining (e.g., DINOv3) and synthetic data. We compare representative models using both quantitative benchmarks and qualitative examples, and discuss natural extensions to video-based depth estimation. Further, to illustrate real-world impact, we highlight the integration of depth estimation into applications such as visual SLAM, content generation, and robot perception. Finally, we outline open challenges and promising research directions as the field advances further into the era of foundation models.

cs.CV↗

arXiv2026

OptiGeo: Efficient Monocular Geometry for Embodied Perception in Optically Challenging Scenes

Monocular depth estimation has achieved strong open-domain generalization, yet reliable robotic deployment remains difficult in transparent, reflective, and specular environments, where depth sensors often produce missing or biased depth. Existing methods often handle such optical failures with scene-specific preprocessing, auxiliary modules, or post-hoc fine-tuning. While effective in constrained settings, these designs increase architectural redundancy and can over-specialize general geometry models to narrow optical scenarios. We revisit this problem as a localized failure mode within base-model training and identify sensor-induced supervision bias as a key bottleneck: models inherit sensor failure patterns from biased real-depth supervision in optically challenging regions. We then introduce OptiGeo, a bias-aware training framework that rehabilitates biased real supervision using a clean-geometry teacher and residual-trimmed alignment. We redefine transparency-targeted rendering as a compact source of clean optical geometry, rather than a large domain-specific fine-tuning set. With only a small targeted rendering set, OptiGeo learns the geometric structure of transparent objects and regions, correcting local geometry distortions that real sensors cannot reliably supervise. Despite only 30M parameters, OptiGeo outperforms substantially larger 300M-scale monocular models and billion-scale multi-view baselines on transparent-scene benchmarks, while remaining competitive on general zero-shot depth and boundary sharpness. Real-world navigation cases further validate its practicality as an efficient perception module in optically challenging scenes.

cs.CV↗

arXiv2026

FoundationGeo: Learning Spatial Pixel-Wise Fields for Monocular Metric Geometry

We present FoundationGeo, a two-stage framework that explicitly bridges relative and metric prediction via spatial calibration and principled data design. Stage 1 learns a high-fidelity, affine-invariant geometry model by initializing with DINOv3 and training on a curated 10.2M-sample multi-domain corpus with complementary local-detail supervision, yielding sharp boundaries and strong cross-domain generalization. Stage 2 moves beyond global scaling by introducing lightweight pixel-wise calibration fields for metric estimation: a scale field for spatially varying metric alignment and a ray-direction correction field that mitigates directional bias in point-map geometry, together producing metrically consistent 3D point maps. Beyond model design, we identify camera intrinsic coverage, especially focal length distribution mismatch between training and test data, as a key bottleneck for zero-shot metric generalization: performance drops sharply when test intrinsics fall outside the training distribution. To address this, we synthesize additional training data across diverse focal lengths using a Blender-based data engine, repairing under-covered focal regimes and improving robustness under intrinsic shift. Extensive zero-shot evaluations across seven benchmarks show that FoundationGeo significantly strengthens cross-domain robustness, staying near the top across diverse domains while avoiding the sharp cross-domain performance drops observed in other methods. This consistency translates into the best overall performance, surpassing heavier baselines by over 5.2% on average.

cs.CV↗

arXiv2026

Stabilizing Streaming Video Geometry via Dynamic Feature Normalization

Consistent 3D geometry estimation from streaming RGB input is crucial for real-world applications such as autonomous driving, embodied AI, and large-scale reconstruction. While modern monocular geometry foundation models achieve strong single-image accuracy, they exhibit severe temporal inconsistency on continuous input, notably dominated by scale--shift drifting. Through targeted empirical analysis, we trace this instability to its root cause: fluctuations in latent feature statistics, whose mean and variance directly determine the predicted depth's scale and shift. Building on this insight, we introduce Dynamic Feature Normalization (DyFN), a lightweight, causal recurrent module that dynamically and robustly modulates feature statistics to maintain stable geometry over time. We adapt powerful pretrained monocular geometry models for streaming by finetuning only DyFN, a mere 2\% additional parameters, while keeping the backbone frozen, thereby achieving temporal consistency without compromising single-image accuracy. Extensive experiments across four benchmarks show that DyFN effectively eliminates temporal artifacts such as disjointed layering and positional jitter, and achieves state-of-the-art temporal stability, improving over prior streaming methods by up to 14\% and even outperforming heavier non-causal video baselines. Project Page: https://shawlyu.github.io/DyFN

cs.CV↗

arXiv2026

PhysEditBench: A Protocol-Conditioned Benchmark for Dense Physical-Map Prediction with Image Editors

Can general-purpose image editors predict physical maps from a single RGB image? General-purpose image editors differ from standard task-specific dense-prediction models: they do not directly take an image and output a physical map. Instead, they must be guided by prompts, examples, or image-based textual cues. To this end, we introduce PhysEditBench, a novel protocol-conditioned benchmark to evaluate and standardize image editors in dense physical-map prediction that covers five targets: depth, normal, albedo, roughness, and metallic maps. For evaluation data, we build a target-dependent benchmark substrate. We use OpenRooms-FF for depth, surface normal, albedo, and roughness, InteriorVerse as an additional source for depth, normal, albedo, and a new procedurally generated source for metallic maps. We curate the data with quality checks, valid-region masks, scene-level sampling, and lighting-based stress subsets to ensure reliable and diverse evaluation. For each target, PhysEditBench defines a fixed protocol that specifies the allowed input, expected output format, and scoring procedure. Each score, therefore, reflects the performance of a model under a specified protocol, rather than its best possible performance under all prompts or interaction modes. Experimental results show that specialized models remain much stronger on depth, normal, and albedo, and stronger image editors can produce more reasonable map-like outputs. For roughness and metallic, image editors can match or outperform specialized baselines on some scalar metrics, but they still suffer from structural errors, sparsity effects, and sensitivity to lighting.

cs.CV↗

arXiv2026

When Verification Fails: How Compositionally Infeasible Claims Escape Rejection

Scientific claim verification, the task of determining whether claims are entailed by scientific evidence, is fundamental to establishing discoveries in evidence while preventing misinformation. This process involves evaluating each asserted constraint against validated evidence. Under the Closed-World Assumption (CWA), a claim is accepted if and only if all asserted constraints are positively supported. We show that existing verification benchmarks cannot distinguish models enforcing this standard from models applying a simpler shortcut called salient-constraint checking, which applies CWA's rejection criterion only to the most salient constraint and accepts when that constraint is supported. Because existing benchmarks construct infeasible claims by perturbing a single salient element they are insufficient at distinguishing between rigorous claim verification and simple salient-constraint reliance. To separate the two, we construct compositionally infeasible claims where the salient constraint is supported but a non-salient constraint is contradicted. Across model families and modalities, models that otherwise saturate existing benchmarks consistently over-accept these claims, confirming the prevalence of such shortcut reasoning. Via model context interventions, we show that different models and prompting strategies occupy distinct positions on a shared ROC curve, indicating that the gap between model families reflects differences in verification threshold rather than underlying reasoning ability, and that the compositional inference bottleneck is a structural property of current verification behavior that strategy guidance alone cannot overcome.

cs.CL↗

arXiv2025

AGN star dynamics under the Influence of Outflow-Ambient Interactions

Stars with outflows interacting with ambient gas experience accelerations arising from the gravitational feedback induced by the interaction structure. In this work, three-dimensional (3D) local shearing box simulations are performed to investigate the dynamical evolution of a star with outflows embedded in the outer regions of an active galactic nucleus (AGN) disk. Two types of stellar wind are considered: isotropic winds and axisymmetric jets, along with variations in the radial pressure gradient profile. The results show that anti-friction enables AGN stars to acquire angular momentum from the ambient gas, resulting in outward migration away from the disk center. The formation and stability of the head-wind structure, which is crucial for maintaining anti-friction, are sensitive to both the strength of the stellar outflow and the radial pressure gradient of the disk gas. Once the head-wind structure is disrupted, the anti-friction effect ceases to operate effectively. A case study is also presented, focusing on a stellar-mass black hole (sBH) in an AGN disk. It is shown that jet material launched along the z-axis is confined to the trailing side of the object's motion by high gas inflow velocities, thereby activating anti-friction and inducing outward migration. If such an sBH migrates inward initially, the interplay between inward and outward migration may trap it at an equilibrium radius, potentially facilitating the formation and merger of black hole binaries.

astro-ph.GA↗

arXiv2024

Open Cluster Dynamics under the Influence of Outflow-Ambient Interactions

Stars with outflows impinging on ambient gas experience accelerations due to the gravitational feedback from the interaction morphology between the outflow and the ambient gas. Such ``negative dynamical friction'' (NDF), in contrast to the conventional ``dynamical friction'' (DF), is studied for its impact on the dynamics of open clusters (OCs) immersed in a uniform ambient gas. We modify the $N$-body integration code \rebound\ with both NDF and DF implemented according to the outflow conditions of each star in a consistently constructed OC. The evolution of stars is also involved in determining the gas-star interactions throughout their stellar lives. Compared to DF-only and gas-free models with identical initial conditions, the NDF-affected cluster is puffier and evaporates faster, as indicated by various diagnostics, including lower velocity dispersions and larger half-mass and half-light radii. Neutron stars with fast winds are expelled from the cluster due to their intensive NDF effect, even without the ``kicks'' by asymmetric supernovae. Exploration of parameter space confirms that the NDF effect is generally enhanced with higher ambient gas densities, in qualitative agreement with the expression of acceleration. Outflow-ambient interactions should be considered for the proper interpretation of the stellar dynamics evolution in clusters.

astro-ph.GA↗

arXiv2024

Stream of Search (SoS): Learning to Search in Language

Language models are rarely shown fruitful mistakes while training. They then struggle to look beyond the next token, suffering from a snowballing of errors and struggling to predict the consequence of their actions several steps ahead. In this paper, we show how language models can be taught to search by representing the process of search in language, as a flattened string -- a stream of search (SoS). We propose a unified language for search that captures an array of different symbolic search strategies. We demonstrate our approach using the simple yet difficult game of Countdown, where the goal is to combine input numbers with arithmetic operations to reach a target number. We pretrain a transformer-based language model from scratch on a dataset of streams of search generated by heuristic solvers. We find that SoS pretraining increases search accuracy by 25% over models trained to predict only the optimal search trajectory. We further finetune this model with two policy improvement methods: Advantage-Induced Policy Alignment (APA) and Self-Taught Reasoner (STaR). The finetuned SoS models solve 36% of previously unsolved problems, including problems that cannot be solved by any of the heuristic solvers. Our results indicate that language models can learn to solve problems via search, self-improve to flexibly use different search strategies, and potentially discover new ones.

cs.LG↗

arXiv2024

LILO: Learning Interpretable Libraries by Compressing and Documenting Code

While large language models (LLMs) now excel at code generation, a key aspect of software development is the art of refactoring: consolidating code into libraries of reusable and readable programs. In this paper, we introduce LILO, a neurosymbolic framework that iteratively synthesizes, compresses, and documents code to build libraries tailored to particular problem domains. LILO combines LLM-guided program synthesis with recent algorithmic advances in automated refactoring from Stitch: a symbolic compression system that efficiently identifies optimal lambda abstractions across large code corpora. To make these abstractions interpretable, we introduce an auto-documentation (AutoDoc) procedure that infers natural language names and docstrings based on contextual examples of usage. In addition to improving human readability, we find that AutoDoc boosts performance by helping LILO's synthesizer to interpret and deploy learned abstractions. We evaluate LILO on three inductive program synthesis benchmarks for string editing, scene reasoning, and graphics composition. Compared to existing neural and symbolic methods - including the state-of-the-art library learning algorithm DreamCoder - LILO solves more complex tasks and learns richer libraries that are grounded in linguistic knowledge.

cs.CL↗

arXiv2022

Dark-siren Cosmology with Decihertz Gravitational-wave Detectors

Gravitational waves (GWs) originated from mergers of stellar-mass binary black holes (SBBHs) are considered as dark sirens in cosmology since they usually do not have electromagnetic counterparts. In order to study cosmos with these events, we not only need the luminosity distances extracted from GW signals, but also require the redshift information of sources via, say, matching GW sky localization with galaxy catalogs. Based on such a methodology, we explore how well decihertz GW detectors, DO-Optimal and DECIGO, can constrain cosmological parameters. Using Monte-Carlo simulated dark sirens, we find that DO-Optimal can constrain the Hubble parameter to ${σ_{H_0}} / {H_0}\, \lesssim 0.23\%$ when estimating $H_0$ alone, while DECIGO performs better by a factor of 5 with ${σ_{H_0}} / {H_0}\lesssim 0.043\%$. Such a good precision of $H_0$ will shed light on the $H_0$ tension. For multiple-parameter estimation, DECIGO can still reach a level of relative uncertainty smaller than $7\%$. The reason why decihertz detectors perform well is explained by their large numbers of SBBH GW events with good distance and angular resolution.

astro-ph.CO↗