Advances in Computer Vision and 3D Reconstruction

The field of computer vision and 3D reconstruction has witnessed significant advancements in recent times, with a focus on developing innovative methods for generating high-quality images, videos, and 3D models. One notable trend is the use of diffusion models, which have shown great promise in image and video generation, as well as 3D reconstruction tasks. These models have been used to generate realistic images and videos, and have also been applied to tasks such as image inpainting, object removal, and novel view synthesis. Another area of research that has gained significant attention is the development of methods for 3D reconstruction from single or multi-view images. These methods have the potential to revolutionize fields such as architecture, product design, and robotics, where accurate 3D models are essential. Noteworthy papers in this area include TanDiT, which proposes a method for generating high-quality 360-degree panoramic images using a tangent-plane diffusion transformer, and AlignCVC, which introduces a framework for aligning cross-view consistency for single-image-to-3D generation. Additionally, methods such as MedDiff-FT and DiGA3D have been proposed for controllable medical image synthesis and versatile 3D inpainting, respectively. Overall, the field of computer vision and 3D reconstruction is rapidly evolving, with new methods and techniques being developed to address various challenges and applications. TanDiT is particularly noteworthy for its ability to generate high-quality panoramic images, while AlignCVC stands out for its ability to align cross-view consistency for single-image-to-3D generation. MedDiff-FT and DiGA3D are also notable for their applications in medical image synthesis and 3D inpainting, respectively.

Sources

TanDiT: Tangent-Plane Diffusion Transformer for High-Quality 360{\deg} Panorama Generation

PrefPaint: Enhancing Image Inpainting through Expert Human Feedback

Quality Assessment and Distortion-aware Saliency Prediction for AI-Generated Omnidirectional Images

Single-Scanline Relative Pose Estimation for Rolling Shutter Cameras

MatChA: Cross-Algorithm Matching with Feature Augmentation

WarpRF: Multi-View Consistency for Training-Free Uncertainty Quantification and Applications in Radiance Fields

3D Shape Generation: A Survey

Single-Frame Point-Pixel Registration via Supervised Cross-Modal Feature Matching

Inpainting is All You Need: A Diffusion-based Augmentation Method for Semi-supervised Medical Image Segmentation

Unsupervised 3D Braided Hair Reconstruction from a Single-View Image

AlignCVC: Aligning Cross-View Consistency for Single-Image-to-3D Generation

MEMFOF: High-Resolution Training for Memory-Efficient Multi-Frame Optical Flow Estimation

PathDiff: Histopathology Image Synthesis with Unpaired Text and Mask Conditions

Time-variant Image Inpainting via Interactive Distribution Transition Estimation

GeoCD: A Differential Local Approximation for Geodesic Chamfer Distance

MTADiffusion: Mask Text Alignment Diffusion Model for Object Inpainting

ViewPoint: Panoramic Video Generation with Pretrained Diffusion Models

WAVE: Warp-Based View Guidance for Consistent Novel View Synthesis Using a Single Image

Towards Initialization-free Calibrated Bundle Adjustment

Refine Any Object in Any Scene

Puzzles: Unbounded Video-Depth Augmentation for Scalable End-to-End 3D Reconstruction

PriOr-Flow: Enhancing Primitive Panoramic Optical Flow with Orthogonal View

TextMesh4D: High-Quality Text-to-4D Mesh Generation

MVGBench: Comprehensive Benchmark for Multi-view Generation Models

MedDiff-FT: Data-Efficient Diffusion Model Fine-tuning with Structural Guidance for Controllable Medical Image Synthesis

Learning Dense Feature Matching via Lifting Single 2D Image to 3D Space

DiGA3D: Coarse-to-Fine Diffusional Propagation of Geometry and Appearance for Versatile 3D Inpainting

BEV-VAE: Multi-view Image Generation with Spatial Consistency for Autonomous Driving

TRACE: Temporally Reliable Anatomically-Conditioned 3D CT Generation with Enhanced Efficiency

Surgical Neural Radiance Fields from One Image

Semiautomatic Simplification

TurboReg: TurboClique for Robust and Efficient Point Cloud Registration

FreeMorph: Tuning-Free Generalized Image Morphing with Diffusion Model

DreamComposer++: Empowering Diffusion Models with Multi-View Conditions for 3D Content Generation

MAGIC: Mask-Guided Diffusion Inpainting with Multi-Level Perturbations and Context-Aware Alignment for Few-Shot Anomaly Generation

Mesh Silksong: Auto-Regressive Mesh Generation as Weaving Silk

MoGe-2: Accurate Monocular Geometry with Metric Scale and Sharp Details

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