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Found 22 papers, 9 papers with code
Learning a Category-level Object Pose Estimator without Pose Annotations
Instead of using manually annotated images, we leverage diffusion models (e. g., Zero-1-to-3) to generate a set of images under controlled pose differences and propose to learn our object pose estimator with those images.
Semantic Flow: Learning Semantic Field of Dynamic Scenes from Monocular Videos
As there is 2D-to-3D ambiguity problem in the viewing direction when extracting 3D flow features from 2D video frames, we consider the volume densities as opacity priors that describe the contributions of flow features to the semantics on the frames.
From Pixel to Cancer: Cellular Automata in Computed Tomography
AI for cancer detection encounters the bottleneck of data scarcity, annotation difficulty, and low prevalence of early tumors.
Radiative Gaussian Splatting for Efficient X-ray Novel View Synthesis
X-ray is widely applied for transmission imaging due to its stronger penetration than natural light.
HISR: Hybrid Implicit Surface Representation for Photorealistic 3D Human Reconstruction
This representation is composed of two surface layers that represent opaque and translucent regions on the clothed human body.
Structure-Aware Sparse-View X-ray 3D Reconstruction
In this paper, we propose a framework, Structure-Aware X-ray Neural Radiodensity Fields (SAX-NeRF), for sparse-view X-ray 3D reconstruction.
Ranked #1 on
Low-Dose X-Ray Ct Reconstruction
on X3D
3D-Aware Neural Body Fitting for Occlusion Robust 3D Human Pose Estimation
Motivated by the recent success of generative models in rigid object pose estimation, we propose 3D-aware Neural Body Fitting (3DNBF) - an approximate analysis-by-synthesis approach to 3D human pose estimation with SOTA performance and occlusion robustness.
Generating Images with 3D Annotations Using Diffusion Models
With explicit 3D geometry control, we can easily change the 3D structures of the objects in the generated images and obtain ground-truth 3D annotations automatically.
Neural Textured Deformable Meshes for Robust Analysis-by-Synthesis
Human vision demonstrates higher robustness than current AI algorithms under out-of-distribution scenarios.
Robust Category-Level 3D Pose Estimation from Synthetic Data
In this work, we aim to narrow the performance gap between models trained on synthetic data and few real images and fully supervised models trained on large-scale data.
Robust 3D-aware Object Classification via Discriminative Render-and-Compare
Using differentiable rendering, we estimate the 3D object pose by minimizing the reconstruction error between the mesh and the feature representation of the target image.
Benchmarking Robustness in Neural Radiance Fields
We find that NeRF-based models are significantly degraded in the presence of corruption, and are more sensitive to a different set of corruptions than image recognition models.
Robust Category-Level 6D Pose Estimation with Coarse-to-Fine Rendering of Neural Features
We consider the problem of category-level 6D pose estimation from a single RGB image.
VoGE: A Differentiable Volume Renderer using Gaussian Ellipsoids for Analysis-by-Synthesis
The Gaussian reconstruction kernels have been proposed by Westover (1990) and studied by the computer graphics community back in the 90s, which gives an alternative representation of object 3D geometry from meshes and point clouds.
OOD-CV: A Benchmark for Robustness to Out-of-Distribution Shifts of Individual Nuisances in Natural Images
One reason is that existing robustness benchmarks are limited, as they either rely on synthetic data or ignore the effects of individual nuisance factors.
Neural View Synthesis and Matching for Semi-Supervised Few-Shot Learning of 3D Pose
The model is initialized from a few labelled images and is subsequently used to synthesize feature representations of unseen 3D views.
NeMo: Neural Mesh Models of Contrastive Features for Robust 3D Pose Estimation
Using differentiable rendering we estimate the 3D object pose by minimizing the reconstruction error between NeMo and the feature representation of the target image.
CoKe: Localized Contrastive Learning for Robust Keypoint Detection
In this paper, we introduce a contrastive learning framework for keypoint detection (CoKe).
Compositional Convolutional Neural Networks: A Robust and Interpretable Model for Object Recognition under Occlusion
The structure of the compositional model enables CompositionalNets to decompose images into objects and context, as well as to further decompose object representations in terms of individual parts and the objects' pose.
Robust Object Detection under Occlusion with Context-Aware CompositionalNets
In this work, we propose to overcome two limitations of CompositionalNets which will enable them to detect partially occluded objects: 1) CompositionalNets, as well as other DCNN architectures, do not explicitly separate the representation of the context from the object itself.
Hyper-Pairing Network for Multi-Phase Pancreatic Ductal Adenocarcinoma Segmentation
Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal cancers with an overall five-year survival rate of 8%.
Weakly Supervised Region Proposal Network and Object Detection
The Convolutional Neural Network (CNN) based region proposal generation method (i. e. region proposal network), trained using bounding box annotations, is an essential component in modern fully supervised object detectors.
