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Found 9 papers, 5 papers with code
The 2021 Image Similarity Dataset and Challenge
This benchmark is used for the Image Similarity Challenge at NeurIPS'21 (ISC2021).
Ranked #1 on
Image Similarity Detection
on DISC21 dev
4D Panoptic LiDAR Segmentation
In this paper, we propose 4D panoptic LiDAR segmentation to assign a semantic class and a temporally-consistent instance ID to a sequence of 3D points.
CIAGAN: Conditional Identity Anonymization Generative Adversarial Networks
In many real-world scenarios like people tracking or action recognition, it is important to be able to process the data while taking careful consideration in protecting people's identity.
Focus on defocus: bridging the synthetic to real domain gap for depth estimation
We are able to train our model completely on synthetic data and directly apply it to a wide range of real-world images.
Ranked #1 on
Depth Estimation
on NYU-Depth V2
(RMSE metric)
Coming Down to Earth: Satellite-to-Street View Synthesis for Geo-Localization
The goal of cross-view image based geo-localization is to determine the location of a given street view image by matching it against a collection of geo-tagged satellite images.
LIME: Live Intrinsic Material Estimation
We present the first end to end approach for real time material estimation for general object shapes with uniform material that only requires a single color image as input.
Deep Appearance Maps
Second, we demonstrate how another network can be used to map from an image or video frames to a DAM network to reproduce this appearance, without using a lengthy optimization such as stochastic gradient descent (learning-to-learn).
Data-Driven but Privacy-Conscious: Pedestrian Dataset De-identification via Full-Body Person Synthesis
To highlight the importance of privacy issues and motivate future research, we motivate and introduce the Pedestrian Dataset De-Identification (PDI) task.
The NeRFect Match: Exploring NeRF Features for Visual Localization
Significantly, we introduce NeRFMatch, an advanced 2D-3D matching function that capitalizes on the internal knowledge of NeRF learned via view synthesis.
