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Found 11 papers, 4 papers with code
2D Feature Distillation for Weakly- and Semi-Supervised 3D Semantic Segmentation
As 3D perception problems grow in popularity and the need for large-scale labeled datasets for LiDAR semantic segmentation increase, new methods arise that aim to reduce the necessity for dense annotations by employing weakly-supervised training.
Prior Based Online Lane Graph Extraction from Single Onboard Camera Image
Thus, online estimation of the lane graph is crucial for widespread and reliable autonomous navigation.
Improving Online Lane Graph Extraction by Object-Lane Clustering
In this work, we propose an architecture and loss formulation to improve the accuracy of local lane graph estimates by using 3D object detection outputs.
Online Lane Graph Extraction from Onboard Video
One of the most common and useful representation of such an understanding is done in the form of BEV lane graphs.
Piecewise Planar Hulls for Semi-Supervised Learning of 3D Shape and Pose from 2D Images
On the one hand, the proposed method learns to segment these planar hulls from the labeled data.
Topology Preserving Local Road Network Estimation from Single Onboard Camera Image
We represent the road topology using a set of directed lane curves and their interactions, which are captured using their intersection points.
End-to-End Learning of Multi-category 3D Pose and Shape Estimation
We use a Transformer-based architecture to detect the keypoints, as well as to summarize the visual context of the image.
Structured Bird's-Eye-View Traffic Scene Understanding from Onboard Images
In this work, we study the problem of extracting a directed graph representing the local road network in BEV coordinates, from a single onboard camera image.
Ranked #3 on
Lane Detection
on nuScenes
Understanding Bird's-Eye View of Road Semantics using an Onboard Camera
In this work, we study scene understanding in the form of online estimation of semantic BEV maps using the video input from a single onboard camera.
Towards Spectral Estimation from a Single RGB Image in the Wild
In contrast to the current literature, we address the problem of estimating the spectrum from a single common trichromatic RGB image obtained under unconstrained settings (e. g. unknown camera parameters, unknown scene radiance, unknown scene contents).
Spectral Estimation From A Single Rgb Image
Spectral Reconstruction
An efficient CNN for spectral reconstruction from RGB images
Recently, the example-based single image spectral reconstruction from RGB images task, aka, spectral super-resolution was approached by means of deep learning by Galliani et al.
