Search Results for author:
Found 29 papers, 11 papers with code
Connecting NeRFs, Images, and Text
Neural Radiance Fields (NeRFs) have emerged as a standard framework for representing 3D scenes and objects, introducing a novel data type for information exchange and storage.
Test Time Training for Industrial Anomaly Segmentation
Anomaly Detection and Segmentation (AD&S) is crucial for industrial quality control.
Deep Learning on 3D Neural Fields
In recent years, Neural Fields (NFs) have emerged as an effective tool for encoding diverse continuous signals such as images, videos, audio, and 3D shapes.
Bottom-Up Instance Segmentation of Catheters for Chest X-Rays
Chest X-ray (CXR) is frequently employed in emergency departments and intensive care units to verify the proper placement of central lines and tubes and to rule out related complications.
Neural Processing of Tri-Plane Hybrid Neural Fields
While processing a field with the same reconstruction quality, we achieve task performance far superior to frameworks that process large MLPs and, for the first time, almost on par with architectures handling explicit representations.
Looking at words and points with attention: a benchmark for text-to-shape coherence
The refined dataset, the new metric and a set of text-shape pairs validated by the user study comprise a novel, fine-grained benchmark that we publicly release to foster research on text-to-shape coherence of text-conditioned 3D generative models.
Depth self-supervision for single image novel view synthesis
In this paper, we tackle the problem of generating a novel image from an arbitrary viewpoint given a single frame as input.
ReLight My NeRF: A Dataset for Novel View Synthesis and Relighting of Real World Objects
By leveraging the dataset, we perform an ablation study on the relighting capability of variants of the vanilla NeRF architecture and identify a lightweight architecture that can render novel views of an object under novel light conditions, which we use to establish a non-trivial baseline for the dataset.
Exploiting the Complementarity of 2D and 3D Networks to Address Domain-Shift in 3D Semantic Segmentation
3D semantic segmentation is a critical task in many real-world applications, such as autonomous driving, robotics, and mixed reality.
Deep Learning on Implicit Neural Representations of Shapes
Implicit Neural Representations (INRs) have emerged in the last few years as a powerful tool to encode continuously a variety of different signals like images, videos, audio and 3D shapes.
Learning Good Features to Transfer Across Tasks and Domains
Besides, we propose a set of strategies to constrain the learned feature spaces, to ease learning and increase the generalization capability of the mapping network, thereby considerably improving the final performance of our framework.
Booster: a Benchmark for Depth from Images of Specular and Transparent Surfaces
Estimating depth from images nowadays yields outstanding results, both in terms of in-domain accuracy and generalization.
Self-Distillation for Unsupervised 3D Domain Adaptation
In contrast, in this work, we focus on obtaining a discriminative feature space for the target domain enforcing consistency between a point cloud and its augmented version.
Cross-Spectral Neural Radiance Fields
We propose X-NeRF, a novel method to learn a Cross-Spectral scene representation given images captured from cameras with different light spectrum sensitivity, based on the Neural Radiance Fields formulation.
RGB-Multispectral Matching: Dataset, Learning Methodology, Evaluation
We address the problem of registering synchronized color (RGB) and multi-spectral (MS) images featuring very different resolution by solving stereo matching correspondences.
Learning the Space of Deep Models
In particular, we show that it is possible to use representation learning to learn a fixed-size, low-dimensional embedding space of trained deep models and that such space can be explored by interpolation or optimization to attain ready-to-use models.
Open Challenges in Deep Stereo: the Booster Dataset
We present a novel high-resolution and challenging stereo dataset framing indoor scenes annotated with dense and accurate ground-truth disparities.
Neural Disparity Refinement for Arbitrary Resolution Stereo
We introduce a novel architecture for neural disparity refinement aimed at facilitating deployment of 3D computer vision on cheap and widespread consumer devices, such as mobile phones.
RefRec: Pseudo-labels Refinement via Shape Reconstruction for Unsupervised 3D Domain Adaptation
Unsupervised Domain Adaptation (UDA) for point cloud classification is an emerging research problem with relevant practical motivations.
Plugging Self-Supervised Monocular Depth into Unsupervised Domain Adaptation for Semantic Segmentation
We further rely on depth to generate a large and varied set of samples to Self-Train the final model.
Shallow Features Guide Unsupervised Domain Adaptation for Semantic Segmentation at Class Boundaries
Although deep neural networks have achieved remarkable results for the task of semantic segmentation, they usually fail to generalize towards new domains, especially when performing synthetic-to-real adaptation.
Learning to Orient Surfaces by Self-supervised Spherical CNNs
In this work, we show the feasibility of learning a robust canonical orientation for surfaces represented as point clouds.
Distilled Semantics for Comprehensive Scene Understanding from Videos
Whole understanding of the surroundings is paramount to autonomous systems.
Boosting Object Recognition in Point Clouds by Saliency Detection
Object recognition in 3D point clouds is a challenging task, mainly when time is an important factor to deal with, such as in industrial applications.
Performance Evaluation of Learned 3D Features
Matching surfaces is a challenging 3D Computer Vision problem typically addressed by local features.
Learning an Effective Equivariant 3D Descriptor Without Supervision
Establishing correspondences between 3D shapes is a fundamental task in 3D Computer Vision, typically addressed by matching local descriptors.
Learning Across Tasks and Domains
Recent works have proven that many relevant visual tasks are closely related one to another.
Learning a Descriptor-Specific 3D Keypoint Detector
Keypoint detection represents the first stage in the majority of modern computer vision pipelines based on automatically established correspondences between local descriptors.
Keypoints from Symmetries by Wave Propagation
The paper conjectures and demonstrates that repeatable keypoints based on salient symmetries at different scales can be detected by a novel analysis grounded on the wave equation rather than the heat equation underlying traditional Gaussian scale-space theory.
