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Claim this profileWorks162 from public data
- 1,281
- D2-Net: A Trainable CNN for Joint Description and Detection of Local Features1,188
This work proposes an approach where a single convolutional neural network plays a dual role: It is simultaneously a dense feature descriptor and a feature detector, and shows that this model can be trained using pixel correspondences extracted from readily available large-scale SfM reconstructions, without any further annotations.
- Mip-Splatting: Alias-Free 3D Gaussian Splatting968
A 3D smoothing filter is introduced to constrains the size of the 3D Gaussian primitives based on the maximal sampling frequency induced by the input views, which eliminates high-frequency artifacts when zooming in.
- D2-Net: A Trainable CNN for Joint Detection and Description of Local Features954
This work proposes an approach where a single convolutional neural network plays a dual role: It is simultaneously a dense feature descriptor and a feature detector, and shows that this model can be trained using pixel correspondences extracted from readily available large-scale SfM reconstructions, without any further annotations.
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- 556
- Image-Based Localization Using LSTMs for Structured Feature Correlation555
Experimental results show the proposed CNN+LSTM architecture for camera pose regression for indoor and outdoor scenes outperforms existing deep architectures, and can localize images in hard conditions, where classic SIFT-based methods fail.
- 473
- Understanding the Limitations of CNN-Based Absolute Camera Pose Regression447
A theoretical model for camera pose regression is developed that is more closely related to pose approximation via image retrieval than to accurate pose estimation via 3D structure, and shows that additional research is needed before pose regression algorithms are ready to compete with structure-based methods.
- Gaussian Opacity Fields: Efficient Adaptive Surface Reconstruction in Unbounded Scenes362
This work presents Gaussian Opacity Fields (GOF), a novel approach for efficient, high-quality, and adaptive surface reconstruction in unbounded scenes, derived from ray-tracing-based volume rendering of 3D Gaussians, enabling direct geometry extraction from 3D Gaussians by identifying its levelset without resorting to Poisson reconstruction or TSDF fusion.
- Back to the Feature: Learning Robust Camera Localization from Pixels to Pose360
PixLoc is introduced, a scene-agnostic neural network that estimates an accurate 6-DoF pose from an image and a 3D model, based on the direct alignment of multiscale deep features, casting camera localization as metric learning.
- Image Retrieval for Image-Based Localization Revisited321
It is shown that retrieval methods using a selective voting scheme are able to outperform state-of-the-art direct matching methods and how both selective voting and correspondence computation can be accelerated by using a Hamming embedding of feature descriptors.
- 320
- Improving Image-Based Localization by Active Correspondence Search309
This work proposes a powerful pipeline for determining the pose of a query image relative to a point cloud reconstruction of a large scene consisting of more than one million 3D points, with run-times comparable or superior to the fastest state-of-the-art methods.
- Semantic Visual Localization285
This paper proposes a novel approach based on a joint 3D geometric and semantic understanding of the world, enabling it to succeed under conditions where previous approaches failed, and employs a novel generative model for descriptor learning.
- Get Out of My Lab: Large-scale, Real-Time Visual-Inertial Localization265
This paper demonstrates that large-scale, real-time pose estimation and tracking can be performed on mobile platforms with limited resources without the use of an external server by employing map and descriptor compression schemes as well as efficient search algorithms from computer vision.
- Patch2Pix: Epipolar-Guided Pixel-Level Correspondences258
This work presents Patch2Pix, a novel refinement network that refines match proposals by regressing pixel-level matches from the local regions defined by those proposals and jointly rejecting outlier matches with confidence scores.
- Night-to-Day Image Translation for Retrieval-based Localization248
This paper proposes ToDayGAN – a modified image-translation model to alter nighttime driving images to a more useful daytime representation, and improves localization performance by over 250% compared the current state-of-the-art, in the context of standard metrics in multiple categories.
- 246
- Scalable 6-DOF Localization on Mobile Devices233
This paper proposes two strategies to combine the information provided by local tracking and global localization with the speed and precision of a local pose tracker on a mobile device that are both scalable and drift-free by design and eliminates the need for loop closure.
- 3D visual perception for self-driving cars using a multi-camera system: Calibration, mapping, localization, and obstacle detection230
This pipeline is able to precisely calibrate multi-camera systems, build sparse 3D maps for visual navigation, visually localize the car with respect to these maps, generate accurate dense maps, as well as detect obstacles based on real-time depth map extraction.
- 213
Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-12. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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