LAMP tracks 3D human motion from moving multi-camera headsets by converting 2D detections to a unified metric 3D world frame via device localization and fitting with an end-to-end spatio-temporal transformer.
citation dossier
Zoedepth: Zero-shot transfer by combining relative and metric depth
18Pith papers citing it
20reference links
cs.CVtop field · 17 papers
UNVERDICTEDtop verdict bucket · 16 papers
why this work matters in Pith
Pith has found this work in 18 reviewed papers. Its strongest current cluster is cs.CV (17 papers). The largest review-status bucket among citing papers is UNVERDICTED (16 papers). For highly cited works, this page shows a dossier first and a bounded explorer second; it never tries to render every citing paper at once.
representative citing papers
Dual-pixel defocus blur enables absolute scale estimation in SfM without reference objects or calibration.
A video generation approach conditions a base model with multi-scale 3D latent features and a cross-attention adapter to produce geometrically realistic and consistent orbital videos from one image.
LiftFormer transforms monocular depth prediction into depth-oriented geometric and edge-aware subspace representations via lifting and frame theory, achieving state-of-the-art results on standard datasets.
3D-VLA is a new embodied foundation model that uses a 3D LLM plus aligned diffusion models to generate future images and point clouds for improved reasoning and action planning in 3D environments.
LILA learns temporally consistent semantic and geometric pixel features from uncurated videos via linear in-context learning on off-the-shelf depth and motion cues, yielding empirical gains on video object segmentation, surface normal estimation, and semantic segmentation.
Layer analysis of DINOv3 shows non-uniform 3D geometric knowledge concentrated in deeper layers, enabling a last-layer-centric recombination module that improves monocular depth estimation accuracy to state-of-the-art levels.
A selective regularization framework lets scale-ambiguous monocular depth priors improve Gaussian Splatting geometry and rendering by isolating and supervising only ill-posed regions.
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.
PAD synthesizes 3D geometry in observation space via depth unprojection as anchor to eliminate pose ambiguity in image-to-3D generation.
A generative video synthesis pipeline paired with a semantic graph neural network yields gains in accident anticipation accuracy and lead time on driving datasets, accompanied by a new benchmark release.
A new wildlife-specific hazy image dataset and IncepDehazeGan model that reports state-of-the-art dehazing metrics and more than doubles downstream animal detection performance.
A multilevel perceptual CRF model using Swin Transformer, HPF fusion, HA adapters, and dynamic scaling attention achieves state-of-the-art monocular depth estimation on NYU Depth v2, KITTI, and MatterPort3D with reduced error and fast inference.
SpatialVLA adds 3D-aware position encoding and adaptive discretized action grids to visual-language-action models, enabling strong zero-shot performance and fine-tuning on new robot setups after pre-training on 1.1 million real-world episodes.
AtteConDA adds attention-based conflict suppression to multi-condition diffusion models so that generated driving-scene images retain richer structural cues from the original annotations.
ELoG-GS integrates geometry-aware initialization and luminance-guided photometric adaptation into Gaussian Splatting, achieving PSNR 18.66 and SSIM 0.69 on the NTIRE 2026 Track 1 low-light 3D reconstruction benchmark.
Step1X-Edit integrates a multimodal LLM with a diffusion decoder, trained on a custom high-quality dataset, to deliver image editing performance that surpasses open-source baselines and approaches proprietary models on the new GEdit-Bench.
citing papers explorer
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LAMP: Localization Aware Multi-camera People Tracking in Metric 3D World
LAMP tracks 3D human motion from moving multi-camera headsets by converting 2D detections to a unified metric 3D world frame via device localization and fitting with an end-to-end spatio-temporal transformer.
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DP-SfM: Dual-Pixel Structure-from-Motion without Scale Ambiguity
Dual-pixel defocus blur enables absolute scale estimation in SfM without reference objects or calibration.
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Towards Realistic and Consistent Orbital Video Generation via 3D Foundation Priors
A video generation approach conditions a base model with multi-scale 3D latent features and a cross-attention adapter to produce geometrically realistic and consistent orbital videos from one image.
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LiftFormer: Lifting and Frame Theory Based Monocular Depth Estimation Using Depth and Edge Oriented Subspace Representation
LiftFormer transforms monocular depth prediction into depth-oriented geometric and edge-aware subspace representations via lifting and frame theory, achieving state-of-the-art results on standard datasets.
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3D-VLA: A 3D Vision-Language-Action Generative World Model
3D-VLA is a new embodied foundation model that uses a 3D LLM plus aligned diffusion models to generate future images and point clouds for improved reasoning and action planning in 3D environments.
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Featurising Pixels from Dynamic 3D Scenes with Linear In-Context Learners
LILA learns temporally consistent semantic and geometric pixel features from uncurated videos via linear in-context learning on off-the-shelf depth and motion cues, yielding empirical gains on video object segmentation, surface normal estimation, and semantic segmentation.
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Last-Layer-Centric Feature Recombination: Unleashing 3D Geometric Knowledge in DINOv3 for Monocular Depth Estimation
Layer analysis of DINOv3 shows non-uniform 3D geometric knowledge concentrated in deeper layers, enabling a last-layer-centric recombination module that improves monocular depth estimation accuracy to state-of-the-art levels.
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In Depth We Trust: Reliable Monocular Depth Supervision for Gaussian Splatting
A selective regularization framework lets scale-ambiguous monocular depth priors improve Gaussian Splatting geometry and rendering by isolating and supervising only ill-posed regions.
-
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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Pose-Aware Diffusion for 3D Generation
PAD synthesizes 3D geometry in observation space via depth unprojection as anchor to eliminate pose ambiguity in image-to-3D generation.
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Learning from the Unseen: Generative Data Augmentation for Geometric-Semantic Accident Anticipation
A generative video synthesis pipeline paired with a semantic graph neural network yields gains in accident anticipation accuracy and lead time on driving datasets, accompanied by a new benchmark release.
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Enhancing Hazy Wildlife Imagery: AnimalHaze3k and IncepDehazeGan
A new wildlife-specific hazy image dataset and IncepDehazeGan model that reports state-of-the-art dehazing metrics and more than doubles downstream animal detection performance.
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Hierarchical Awareness Adapters with Hybrid Pyramid Feature Fusion for Dense Depth Prediction
A multilevel perceptual CRF model using Swin Transformer, HPF fusion, HA adapters, and dynamic scaling attention achieves state-of-the-art monocular depth estimation on NYU Depth v2, KITTI, and MatterPort3D with reduced error and fast inference.
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SpatialVLA: Exploring Spatial Representations for Visual-Language-Action Model
SpatialVLA adds 3D-aware position encoding and adaptive discretized action grids to visual-language-action models, enabling strong zero-shot performance and fine-tuning on new robot setups after pre-training on 1.1 million real-world episodes.
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AtteConDA: Attention-Based Conflict Suppression in Multi-Condition Diffusion Models and Synthetic Data Augmentation
AtteConDA adds attention-based conflict suppression to multi-condition diffusion models so that generated driving-scene images retain richer structural cues from the original annotations.
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ELoG-GS: Dual-Branch Gaussian Splatting with Luminance-Guided Enhancement for Extreme Low-light 3D Reconstruction
ELoG-GS integrates geometry-aware initialization and luminance-guided photometric adaptation into Gaussian Splatting, achieving PSNR 18.66 and SSIM 0.69 on the NTIRE 2026 Track 1 low-light 3D reconstruction benchmark.
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Step1X-Edit: A Practical Framework for General Image Editing
Step1X-Edit integrates a multimodal LLM with a diffusion decoder, trained on a custom high-quality dataset, to deliver image editing performance that surpasses open-source baselines and approaches proprietary models on the new GEdit-Bench.
- SS3D: End2End Self-Supervised 3D from Web Videos