MODEST provides the first large-scale high-resolution stereo DSLR dataset with systematic variation of focal length and aperture to support research on real-world optical effects in depth estimation.
Scannet: Richly-annotated 3d reconstructions of indoor scenes
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
representative citing papers
FreeOcc enables training-free open-vocabulary 3D occupancy prediction from RGB-D sequences by combining SLAM, dense Gaussian maps, off-the-shelf vision-language models, and probabilistic projection, achieving over 2x gains on benchmarks and zero-shot transfer to novel scenes.
Zero-shot DINOv3 features, many-to-many candidate matching, and Harmonic Consensus Maximization give out-of-domain camera pose accuracy comparable to supervised matchers.
citing papers explorer
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MODEST: Multi-Optics Depth-of-Field Stereo Dataset
MODEST provides the first large-scale high-resolution stereo DSLR dataset with systematic variation of focal length and aperture to support research on real-world optical effects in depth estimation.
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FreeOcc: Training-Free Embodied Open-Vocabulary Occupancy Prediction
FreeOcc enables training-free open-vocabulary 3D occupancy prediction from RGB-D sequences by combining SLAM, dense Gaussian maps, off-the-shelf vision-language models, and probabilistic projection, achieving over 2x gains on benchmarks and zero-shot transfer to novel scenes.
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Zero-Shot DINOv3-Based Image Matching via Many-to-Many Association
Zero-shot DINOv3 features, many-to-many candidate matching, and Harmonic Consensus Maximization give out-of-domain camera pose accuracy comparable to supervised matchers.