Deep depth completion from extremely sparse data: A survey
Depth completion aims at predicting dense pixel-wise depth from an extremely sparse map
captured from a depth sensor, eg, LiDARs. It plays an essential role in various applications …
captured from a depth sensor, eg, LiDARs. It plays an essential role in various applications …
Self-supervised multimodal learning: A survey
Multimodal learning, which aims to understand and analyze information from multiple
modalities, has achieved substantial progress in the supervised regime in recent years …
modalities, has achieved substantial progress in the supervised regime in recent years …
Bridging the domain gap: Self-supervised 3d scene understanding with foundation models
Foundation models have achieved remarkable results in 2D and language tasks like image
segmentation, object detection, and visual-language understanding. However, their …
segmentation, object detection, and visual-language understanding. However, their …
Camliflow: bidirectional camera-lidar fusion for joint optical flow and scene flow estimation
In this paper, we study the problem of jointly estimating the optical flow and scene flow from
synchronized 2D and 3D data. Previous methods either employ a complex pipeline that …
synchronized 2D and 3D data. Previous methods either employ a complex pipeline that …
Real time object detection using LiDAR and camera fusion for autonomous driving
H Liu, C Wu, H Wang - Scientific Reports, 2023 - nature.com
Autonomous driving has been widely applied in commercial and industrial applications,
along with the upgrade of environmental awareness systems. Tasks such as path planning …
along with the upgrade of environmental awareness systems. Tasks such as path planning …
Naruto: Neural active reconstruction from uncertain target observations
We present NARUTO a neural active reconstruction system that combines a hybrid neural
representation with uncertainty learning enabling high-fidelity surface reconstruction. Our …
representation with uncertainty learning enabling high-fidelity surface reconstruction. Our …
CVRecon: Rethinking 3d geometric feature learning for neural reconstruction
Recent advances in neural reconstruction using posed image sequences have made
remarkable progress. However, due to the lack of depth information, existing volumetric …
remarkable progress. However, due to the lack of depth information, existing volumetric …
Learning optical flow and scene flow with bidirectional camera-lidar fusion
In this paper, we study the problem of jointly estimating the optical flow and scene flow from
synchronized 2D and 3D data. Previous methods either employ a complex pipeline that …
synchronized 2D and 3D data. Previous methods either employ a complex pipeline that …
Point cloud self-supervised learning via 3d to multi-view masked autoencoder
In recent years, the field of 3D self-supervised learning has witnessed significant progress,
resulting in the emergence of Multi-Modality Masked AutoEncoders (MAE) methods that …
resulting in the emergence of Multi-Modality Masked AutoEncoders (MAE) methods that …
A comprehensive survey of depth completion approaches
Depth maps produced by LiDAR-based approaches are sparse. Even high-end LiDAR
sensors produce highly sparse depth maps, which are also noisy around the object …
sensors produce highly sparse depth maps, which are also noisy around the object …