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09/14/2023
Looking at words and points with attention: a benchmark for text-to-shape coherence
While text-conditional 3D object generation and manipulation have seen r...
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08/27/2023
Depth self-supervision for single image novel view synthesis
In this paper, we tackle the problem of generating a novel image from an...
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04/20/2023
ReLight My NeRF: A Dataset for Novel View Synthesis and Relighting of Real World Objects
In this paper, we focus on the problem of rendering novel views from a N...
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04/06/2023
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 applicati...
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02/10/2023
Deep Learning on Implicit Neural Representations of Shapes
Implicit Neural Representations (INRs) have emerged in the last few year...
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01/26/2023
Learning Good Features to Transfer Across Tasks and Domains
Availability of labelled data is the major obstacle to the deployment of...
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01/19/2023
Booster: a Benchmark for Depth from Images of Specular and Transparent Surfaces
Estimating depth from images nowadays yields outstanding results, both i...
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10/15/2022
Self-Distillation for Unsupervised 3D Domain Adaptation
Point cloud classification is a popular task in 3D vision. However, prev...
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09/01/2022
Cross-Spectral Neural Radiance Fields
We propose X-NeRF, a novel method to learn a Cross-Spectral scene repres...
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06/14/2022
RGB-Multispectral Matching: Dataset, Learning Methodology, Evaluation
We address the problem of registering synchronized color (RGB) and multi...
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06/10/2022
Learning the Space of Deep Models
Embedding of large but redundant data, such as images or text, in a hier...
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06/09/2022
Open Challenges in Deep Stereo: the Booster Dataset
We present a novel high-resolution and challenging stereo dataset framin...
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10/28/2021
Neural Disparity Refinement for Arbitrary Resolution Stereo
We introduce a novel architecture for neural disparity refinement aimed ...
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10/21/2021
RefRec: Pseudo-labels Refinement via Shape Reconstruction for Unsupervised 3D Domain Adaptation
Unsupervised Domain Adaptation (UDA) for point cloud classification is a...
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10/13/2021
Plugging Self-Supervised Monocular Depth into Unsupervised Domain Adaptation for Semantic Segmentation
Although recent semantic segmentation methods have made remarkable progr...
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10/06/2021
Shallow Features Guide Unsupervised Domain Adaptation for Semantic Segmentation at Class Boundaries
Although deep neural networks have achieved remarkable results for the t...
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11/06/2020
Learning to Orient Surfaces by Self-supervised Spherical CNNs
Defining and reliably finding a canonical orientation for 3D surfaces is...
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03/31/2020
Distilled Semantics for Comprehensive Scene Understanding from Videos
Whole understanding of the surroundings is paramount to autonomous syste...
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11/06/2019
Boosting Object Recognition in Point Clouds by Saliency Detection
Object recognition in 3D point clouds is a challenging task, mainly when...
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09/15/2019
Learning an Effective Equivariant 3D Descriptor Without Supervision
Establishing correspondences between 3D shapes is a fundamental task in ...
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09/15/2019
Performance Evaluation of Learned 3D Features
Matching surfaces is a challenging 3D Computer Vision problem typically ...
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04/09/2019