AmbiSuR adds intrinsic photometric disambiguation and a self-indication module to Gaussian Splatting to resolve ambiguities and improve surface reconstruction accuracy.
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11 Pith papers cite this work. Polarity classification is still indexing.
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LAGRNet embeds learnable algebraic group, ring, and sheaf structures into a neural network to improve accuracy and generalization in monocular depth estimation.
A new benchmark with real lunar stereo ground truth and analog data shows that sim-to-real fine-tuned monocular depth models achieve large in-domain gains but minimal generalization to actual lunar images.
GemDepth predicts inter-frame camera poses to inject geometric embeddings into a spatio-temporal transformer, yielding state-of-the-art 3D-consistent video depth.
SS3D pretrains an end-to-end feed-forward 3D estimator on filtered YouTube-8M videos via SfM self-supervision, MVS filtering, and expert distillation, delivering stronger zero-shot transfer and fine-tuning than prior self-supervised baselines.
A two-module neural model disentangles spatial layout from material properties to generate controllable and more realistic room impulse responses, reporting gains of up to 16% on acoustic metrics and 70% on material metrics plus better human ratings.
Depth Anything V2 delivers finer, more robust monocular depth predictions by replacing real labeled images with synthetic data, scaling the teacher model, and using large-scale pseudo-labeled real images for student training.
MTD turns relative depth into metric depth via segment-wise sparse graph optimization and discontinuity-aware geodesic pixel refinement, claiming better accuracy and generalization than prior depth methods.
A drone-mounted stereo camera pipeline with YOLO segmentation, deep stereo depth, centroid triangulation, and MAD outlier rejection achieves robust 3D positioning of thin pine branches at 1-2 m distances.
MoGe-2 recovers metric-scale 3D point maps with fine details from single images via data refinement and extension of affine-invariant predictions.
Drone stereo vision pipeline segments pine branches with YOLO variants and estimates depth with deep stereo networks, yielding more coherent maps than SGBM at 1-2 m distances.
citing papers explorer
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Revisiting Photometric Ambiguity for Accurate Gaussian-Splatting Surface Reconstruction
AmbiSuR adds intrinsic photometric disambiguation and a self-indication module to Gaussian Splatting to resolve ambiguities and improve surface reconstruction accuracy.
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Monocular Depth Estimation via Neural Network with Learnable Algebraic Group and Ring Structures
LAGRNet embeds learnable algebraic group, ring, and sheaf structures into a neural network to improve accuracy and generalization in monocular depth estimation.
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LuMon: A Comprehensive Benchmark and Development Suite with Novel Datasets for Lunar Monocular Depth Estimation
A new benchmark with real lunar stereo ground truth and analog data shows that sim-to-real fine-tuned monocular depth models achieve large in-domain gains but minimal generalization to actual lunar images.
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GemDepth: Geometry-Embedded Features for 3D-Consistent Video Depth
GemDepth predicts inter-frame camera poses to inject geometric embeddings into a spatio-temporal transformer, yielding state-of-the-art 3D-consistent video depth.
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SS3D: End2End Self-Supervised 3D from Web Videos
SS3D pretrains an end-to-end feed-forward 3D estimator on filtered YouTube-8M videos via SfM self-supervision, MVS filtering, and expert distillation, delivering stronger zero-shot transfer and fine-tuning than prior self-supervised baselines.
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Materialistic RIR: Material Conditioned Realistic RIR Generation
A two-module neural model disentangles spatial layout from material properties to generate controllable and more realistic room impulse responses, reporting gains of up to 16% on acoustic metrics and 70% on material metrics plus better human ratings.
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Depth Anything V2
Depth Anything V2 delivers finer, more robust monocular depth predictions by replacing real labeled images with synthetic data, scaling the teacher model, and using large-scale pseudo-labeled real images for student training.
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The Midas Touch for Metric Depth
MTD turns relative depth into metric depth via segment-wise sparse graph optimization and discontinuity-aware geodesic pixel refinement, claiming better accuracy and generalization than prior depth methods.
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Low-Cost Stereo Vision for Robust 3D Positioning of Thin Radiata Pine Branches in Autonomous Drone Pruning
A drone-mounted stereo camera pipeline with YOLO segmentation, deep stereo depth, centroid triangulation, and MAD outlier rejection achieves robust 3D positioning of thin pine branches at 1-2 m distances.
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MoGe-2: Accurate Monocular Geometry with Metric Scale and Sharp Details
MoGe-2 recovers metric-scale 3D point maps with fine details from single images via data refinement and extension of affine-invariant predictions.
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Positioning radiata pine branches requiring pruning by drone stereo vision
Drone stereo vision pipeline segments pine branches with YOLO variants and estimates depth with deep stereo networks, yielding more coherent maps than SGBM at 1-2 m distances.