✨Depth Anything 3: Recovering the Visual Space from Any Views

📝 Summary:
Depth Anything 3 DA3 predicts spatially consistent geometry from any visual inputs, even without known camera poses. It uses a plain transformer backbone and a singular depth-ray prediction target. DA3 achieves new state-of-the-art results on a visual geometry benchmark, outperforming previous mo...

🔹 Publication Date: Published on Nov 13

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.10647
• PDF: https://arxiv.org/pdf/2511.10647

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#ComputerVision #DepthEstimation #AIResearch #Transformers #3DReconstruction

✨Diffusion Knows Transparency: Repurposing Video Diffusion for Transparent Object Depth and Normal Estimation

📝 Summary:
Transparent objects are hard for perception. This work observes video diffusion models can synthesize transparent phenomena, so they repurpose one. Their DKT model, trained on a new dataset, achieves zero-shot SOTA for depth and normal estimation of transparent objects, proving diffusion knows tr...

🔹 Publication Date: Published on Dec 29

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.23705
• PDF: https://arxiv.org/pdf/2512.23705
• Project Page: https://daniellli.github.io/projects/DKT/
• Github: https://github.com/Daniellli/DKT

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#ComputerVision #DiffusionModels #DepthEstimation #TransparentObjects #AIResearch

✨InfiniDepth: Arbitrary-Resolution and Fine-Grained Depth Estimation with Neural Implicit Fields

📝 Summary:
InfiniDepth introduces neural implicit fields for continuous 2D depth querying, overcoming limitations of discrete grid methods. This enables arbitrary-resolution and fine-grained depth estimation, achieving state-of-the-art performance, particularly in fine-detail regions and for novel view synt...

🔹 Publication Date: Published on Jan 6

🔹 Paper Links:
• arXiv Page: https://arxivexplained.com/papers/infinidepth-arbitrary-resolution-and-fine-grained-depth-estimation-with-neural-implicit-fields
• PDF: https://arxiv.org/pdf/2601.03252
• Project Page: https://zju3dv.github.io/InfiniDepth
• Github: https://zju3dv.github.io/InfiniDepth

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#DepthEstimation #NeuralImplicitFields #ComputerVision #AI #3DGraphics

🔥 VLM3: Vision Language Models Are Native 3D Learners

💡 The paper VLM3 Vision Language Models Are Native 3D Learners presents a study that challenges the common approach to 3D understanding tasks in computer vision. Typically these tasks rely on specialized vision models with complex designs and extensive data augmentation. However the authors argue that vision language models can be adapted for 3D understanding tasks through simple architectural modifications and text-based training.

The problem addressed in this paper is that 3D understanding tasks such as depth estimation and object-level 3D understanding are currently dominated by expert vision models that have complex task-specific designs. The authors propose that vision language models can be native 3D learners and achieve comparable performance to these specialized models.

The method used in this study involves making three simple modifications to standard vision language models. These modifications include focal length unification, text-based pixel reference, and data mixture and scaling. The authors propose VLM3, a scalable method that enables standard vision language models to master diverse 3D tasks without requiring complex designs or extensive data augmentation.

The results of the study show that VLM3 advances the depth estimation accuracy of vision language models by a large margin, from 0.84 to 0.9. Additionally, VLM3 enables diverse 3D tasks such as pixel correspondence, camera pose estimation, and object-level 3D understanding, matching the accuracy of expert vision models while maintaining standard architectures and text-based training. Overall, the paper presents a new paradigm for simple and scalable 3D learning, demonstrating that vision language models can be effective native 3D learners.

📅 Published on May 28

🔗 Links:
• GitHub: https://github.com/huggingface
• arXiv: https://arxiv.org/abs/2605.30561
• PDF: https://arxiv.org/pdf/2605.30561

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📢 By: https://xn--r1a.website/PaperNexus

#VisionLanguageModels #3DUnderstanding #DepthEstimation #ObjectLevel3D #ComputerVisionModels