Pumpire: Unified Benchmark for Metric Distance Estimation

arXiv:2610.12423 · cs.CV · Submitted 2026-10-08 · Read on arXiv

cs.CV

Submitted: 2026-10-08

Updated: 2026-10-08

Project page: https://pumpire.github.io

Terminology

• Benchmark — 37× in this paper · explained in 2 episodes
• Foundation Model — 21× in this paper · explained in 3 episodes
• Foundation Models — 21× in this paper · explained in 7 episodes
• Accuracy — 17× in this paper · explained in 1 episode
• Conference on Computer Vision and Pattern Recognition — 9× in this paper
• Consistency — 8× in this paper · explained in 3 episodes
• Robustness — 7× in this paper · explained in 5 episodes
• Benchmarking — 6× in this paper · explained in 3 episodes
• Sparsity — 5× in this paper · explained in 4 episodes
• Generalization — 3× in this paper · explained in 13 episodes
• AUC — 2× in this paper · explained in 2 episodes
• Absolute Error — 2× in this paper
• Adaptation — 2× in this paper · explained in 1 episode
• CVF International Conference on Computer Vision — 2× in this paper
• Generative AI — 2× in this paper · explained in 2 episodes
• Verification — 2× in this paper · explained in 2 episodes
• Alignment — 1× in this paper · explained in 1 episode
• Calibration — 1× in this paper · explained in 7 episodes
• Core — 1× in this paper · explained in 2 episodes
• Fidelity — 1× in this paper · explained in 2 episodes
• Gaussian Splatting — 1× in this paper · explained in 4 episodes
• Grounding — 1× in this paper · explained in 3 episodes
• Human-in-the-Loop — 1× in this paper · explained in 3 episodes
• Logarithmic Error — 1× in this paper
• Multimodal Dataset — 1× in this paper · explained in 2 episodes
• Pseudo-labeling — 1× in this paper · explained in 3 episodes
• Reconstruction Error — 1× in this paper · explained in 2 episodes
• Redundancy — 1× in this paper · explained in 2 episodes
• Relative Error — 1× in this paper
• Relative Standard Deviation — 1× in this paper
• Transformer — 1× in this paper · explained in 2 episodes

Sources

• Joint 2D-3D-Semantic Data for Indoor Scene Understanding
• E3D-Bench: A Benchmark for End-to-End 3D Geometric Foundation Models
• Reliability-aware Execution Gating for Near-field and Off-axis Vision-guided Robotic Alignment
• Depth Completion as Parameter-Efficient Test-Time Adaptation
• BenchDepth: Are We on the Right Way to Evaluate Depth Foundation Models?
• Depth Anything 3: Recovering the Visual Space from Any Views
• Manipulation as in Simulation: Enabling Accurate Geometry Perception in Robots
• DepthLab: From Partial to Complete
• MetricAnything: Scaling Metric Depth Pretraining with Noisy Heterogeneous Sources
• Collision Avoidance Metric for 3D Camera Evaluation
• DIODE: A Dense Indoor and Outdoor DEpth Dataset
• AURORA-KITTI: Any-Weather Depth Completion and Denoising in the Wild
• Depth Anything with Any Prior
• EarthSpatialBench: Benchmarking Spatial Reasoning Capabilities of Multimodal LLMs on Earth Imagery
• InfiniDepth: Arbitrary-Resolution and Fine-Grained Depth Estimation with Neural Implicit Fields

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