Humanoid-OmniOcc delivers a large-scale panoramic stereo occupancy dataset for humanoid robots via Real2Sim2Real, with a model that outperforms monocular baselines in both unseen sim scenes and real settings.
Stereo anything: Unifying stereo matching with large-scale mixed data
7 Pith papers cite this work. Polarity classification is still indexing.
7
Pith papers citing it
verdicts
UNVERDICTED 7representative citing papers
A unified dual-shutter framework and dual-stream network jointly correct blur and rolling-shutter distortion to reconstruct high-speed video from real-world degraded image pairs.
PicoEyes delivers a unified end-to-end model for full 3D gaze estimation including eye parameters, axes, segmentation and depth from monocular or binocular near-eye images, supported by a new large-scale multi-view dataset.
StereoFactory merges stereo matching foundation models via genetic subset search followed by CMA-ES module routing, reporting lower average errors on four benchmarks than baselines while using 2.7-3.7% of retraining time.
Lite Any Stereo delivers top-ranked zero-shot accuracy on four real-world stereo benchmarks using a lightweight backbone, hybrid cost aggregation, and three-stage training on million-scale data, at less than 1% of typical computational cost.
LAS2 is a series of efficient stereo matching models that reach state-of-the-art zero-shot performance among fast methods while running 1.8-2.7x faster than prior iterative approaches on H200 and Orin hardware.
ROVR is a new diverse depth dataset for autonomous driving with 200K frames, released pipelines, and ablations showing sparse ground truth supports model training.
citing papers explorer
No citing papers match the current filters.