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Found 17 papers, 7 papers with code
3DGS-Calib: 3D Gaussian Splatting for Multimodal SpatioTemporal Calibration
We introduce 3DGS-Calib, a new calibration method that relies on the speed and rendering accuracy of 3D Gaussian Splatting to achieve multimodal spatiotemporal calibration that is accurate, robust, and with a substantial speed-up compared to methods relying on implicit neural representations.
SOAC: Spatio-Temporal Overlap-Aware Multi-Sensor Calibration using Neural Radiance Fields
In this paper, we leverage the ability of Neural Radiance Fields (NeRF) to represent different sensors modalities in a common volumetric representation to achieve robust and accurate spatio-temporal sensor calibration.
Joint 3D Shape and Motion Estimation from Rolling Shutter Light-Field Images
In this paper, we propose an approach to address the problem of 3D reconstruction of scenes from a single image captured by a light-field camera equipped with a rolling shutter sensor.
Alignment-free HDR Deghosting with Semantics Consistent Transformer
The spatial attention aims to deal with the intra-image correlation to model the dynamic motion, while the channel attention enables the inter-image intertwining to enhance the semantic consistency across frames.
Object Segmentation by Mining Cross-Modal Semantics
In this paper, we propose a novel approach by mining the Cross-Modal Semantics to guide the fusion and decoding of multimodal features, with the aim of controlling the modal contribution based on relative entropy.
Mobile Mapping Mesh Change Detection and Update
However it has two drawbacks: 1) the old data may be of higher quality (resolution, precision) than the new and 2) the coverage of the scene might be different in various acquisitions, including varying occlusions.
MOISST: Multimodal Optimization of Implicit Scene for SpatioTemporal calibration
With the recent advances in autonomous driving and the decreasing cost of LiDARs, the use of multimodal sensor systems is on the rise.
HiDAnet: RGB-D Salient Object Detection via Hierarchical Depth Awareness
In this paper, from a new perspective, we propose a novel Hierarchical Depth Awareness network (HiDAnet) for RGB-D saliency detection.
Source-free Depth for Object Pop-out
In this work, we adapt such depth inference models for object segmentation using the objects' "pop-out" prior in 3D.
RGB-Event Fusion for Moving Object Detection in Autonomous Driving
Moving Object Detection (MOD) is a critical vision task for successfully achieving safe autonomous driving.
Robust RGB-D Fusion for Saliency Detection
In this work, we aim for RGB-D saliency detection that is robust to the low-quality depths which primarily appear in two forms: inaccuracy due to noise and the misalignment to RGB.
Depth-Adapted CNNs for RGB-D Semantic Segmentation
Recent RGB-D semantic segmentation has motivated research interest thanks to the accessibility of complementary modalities from the input side.
Ranked #41 on
Semantic Segmentation
on NYU Depth v2
N-QGN: Navigation Map from a Monocular Camera using Quadtree Generating Networks
Monocular depth estimation has been a popular area of research for several years, especially since self-supervised networks have shown increasingly good results in bridging the gap with supervised and stereo methods.
Modality-Guided Subnetwork for Salient Object Detection
Recent RGBD-based models for saliency detection have attracted research attention.
Unsupervised Learning of Category-Specific Symmetric 3D Keypoints from Point Sets
This paper aims at learning category-specific 3D keypoints, in an unsupervised manner, using a collection of misaligned 3D point clouds of objects from an unknown category.
What's in my Room? Object Recognition on Indoor Panoramic Images
In the last few years, there has been a growing interest in taking advantage of the 360 panoramic images potential, while managing the new challenges they imply.
Corners for Layout: End-to-End Layout Recovery from 360 Images
The problem of 3D layout recovery in indoor scenes has been a core research topic for over a decade.
