Metric Scale
No mentions found
This entity hasn't been tracked yet, or Iris is still building its knowledge base.
Related Articles from SNS
Empowering Feed-Forward Reconstruction Models with Metric Scale via Satellite Images
arXiv:2606.08205v1 Announce Type: new Abstract: Feed-forward 3D reconstruction models have recently shown strong generalization across diverse scenes, yet most of them recover geometry only up to an unknown global scale. This scale ambiguity limits their use in applications that require metric understanding of the environment. Existing metric reconstruction methods commonly rely on large-scale metric annotations or accurate camera calibration, both of which are costly or unreliable in many...
Clustering in atom probe tomography data: coordination number metric, percolation-based parameter scaling, and size effects
Announce Type: cross Abstract: The ability to identify nanometer-scale nuclei of new phases in atom probe tomography (APT) is often limited by the sensitivity of clustering algorithms to user-defined control parameters. Conventional approaches typically rely on the Euclidean distance metric and consider only solute atoms, thereby discarding the solvent atoms that contain most of the spatial information. Here, we introduce a coordination-number metric based on the composition and apply it to...
Honey, I Shrunk the Arc de Triomphe!
arXiv:2606.02379v1 Announce Type: new Abstract: Metric scale monocular geometry estimation has seen significant progress through large-scale data aggregation, yet current foundation models suffer from a persistent ''scale-collapse'' phenomenon: distant landmarks and vast landscapes are metrically underestimated. We hypothesize that this performance gap stems from a training data bottleneck, where existing metric-scale datasets are hardware-constrained to homogenous vehicle-captured LiDAR or...
BEV-ODOM2: Enhanced BEV-based Monocular Visual Odometry with PV-BEV Fusion and Dense Flow Supervision for Ground Robots
Announce Type: replace Abstract: Scale-consistent ego-motion estimation is fundamental for autonomous ground robots. Bird's-Eye-View (BEV) representation naturally addresses the scale drift problem of monocular visual odometry (MVO) by providing a metric-scaled planar workspace, enabling the simplification of 6-DoF ego-motion to a more robust 3-DoF model. However, existing BEV-based methods suffer from two key limitations: sparse supervision signals from pose-only training, and information...
The Hypocrisy of the Democrats Who Defend Graham Platner
Title: The Hypocrisy of the Democrats Who Defend Graham Platner For decades, Nazism and the anti-Semitism underlying it have marked zero on the Kelvin scale of villainy—the metric against which all other forms of evil are compared. This is so well understood that we now have cultural phenomena such as Godwin’s Law, the theory that online debates inevitably lead to Nazi comparisons, and the “everything I don’t like is Hitler” meme. But their existence proves the point: If one wishes to say that something is irredeemably bad, Nazis are the benchmark, the absolute.Yet recently this understanding seems to have grown less universal. Nazi symbolism and more modern versions of the ancient conspiracy theories behind this intolerable ideology have found a degree of toleration within American political movements desperate for shortsighted victories. The underlying hatred that, among other things, motivated the killing of more than a third of all the Jews on the planet eight decades ago is viewed no longer as unacceptable, but rather somewhere on a scale of “problematic” issues that can be either explained away or ignored.The most recent case is that of Graham Platner, the 41-year-old Democrat who is hoping to unseat Senator Susan Collins in Maine. Platner has a unique personal
Robust Scene Transfer for PointGoal Navigation via Privileged Sensor Guided Contrastive Learning
arXiv:2606.05506v1 Announce Type: new Abstract: We propose a sensor-guided adaptive contrastive learning framework for visual representation learning in PointGoal navigation. During training, privileged LiDAR sensing guides the contrastive objective through a geometry-aware similarity metric and adaptive temperature scaling, encouraging visual embeddings to capture navigation-relevant structure rather than scene-specific appearance. The resulting encoder is pretrained independently, frozen,...
Agreement Metrics for LLM-as-Judge Evaluation: What to Report and Why
arXiv:2606.00093v1 Announce Type: cross Abstract: Validating an LLM judge against human annotations usually means reporting several agreement statistics: accuracy, precision, recall, $F_1$, Cohen's $\kappa$, and one or more rank correlations. A survey of 24 recent LLM-as-judge papers finds metric choice entangled with the judgment scale, tie handling, invalid outputs, and abstention handling, and those choices rarely stated. For binary criteria -- the common case in rubric-based evaluation,...
Direct Informed Sampling on Riemannian Manifolds via Loewner Order Lower Bounds
arXiv:2606.02879v1 Announce Type: new Abstract: Informed sampling techniques accelerate sampling-based motion planners by focusing the search on promising regions of the state space, yet most existing methods rely on Euclidean heuristics that become inadmissible under configuration-dependent Riemannian metrics. While scalar eigenvalue bounds restore admissibility by uniformly scaling the Euclidean distance, they discard the directional structure of the metric, producing overly conservative...
Exploring Flow-Lenia Universes with a Curiosity-driven AI Scientist: Discovering Diverse Ecosystem Dynamics
arXiv:2505.15998v4 Announce Type: replace Abstract: We present a curiosity-driven AI scientist method for discovering system-level dynamics in Flow-Lenia, a continuous cellular automaton (CA) with mass conservation and parameter localization. Building on prior work that uses diversity search in Lenia to find individual self-organized patterns, we adapt Intrinsically Motivated Goal Exploration Processes (IMGEPs) to large environments of interacting patterns, using simulation-wide metrics such...
PerBite: A Curated Diagnostic Workflow for Bite-Aware Food Volume Estimation
Announce Type: new Abstract: Can a visually plausible food mesh be trusted to estimate the volume of consumed food? \method investigates this question using selected paired before- and after-consumption states from the MetaFood CVPR 2026 Continuous 3D Reconstruction While Eating Challenge. The submitted workflow follows a curated reconstruction protocol: SAM~3 segments the food and plate regions; Hunyuan3D/SAM~3D generates a dimensionless food mesh; the plate diameter provides the metric...