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Found 10 papers, 6 papers with code
Occupancy-MAE: Self-supervised Pre-training Large-scale LiDAR Point Clouds with Masked Occupancy Autoencoders
This work proposes a solution to reduce the dependence on labelled 3D training data by leveraging pre-training on large-scale unlabeled outdoor LiDAR point clouds using masked autoencoders (MAE).
UniScene: Multi-Camera Unified Pre-training via 3D Scene Reconstruction
When compared to monocular pre-training methods on the nuScenes dataset, UniScene shows a significant improvement of about 2. 0% in mAP and 2. 0% in NDS for multi-camera 3D object detection, as well as a 3% increase in mIoU for surrounding semantic scene completion.
Attentional Graph Neural Network for Parking-slot Detection
Deep learning has recently demonstrated its promising performance for vision-based parking-slot detection.
ORFD: A Dataset and Benchmark for Off-Road Freespace Detection
Freespace detection is an essential component of autonomous driving technology and plays an important role in trajectory planning.
AA-RMVSNet: Adaptive Aggregation Recurrent Multi-view Stereo Network
To overcome the difficulty of varying occlusion in complex scenes, we propose an inter-view cost volume aggregation module for adaptive pixel-wise view aggregation, which is able to preserve better-matched pairs among all views.
Ranked #10 on
Point Clouds
on Tanks and Temples
(Mean F1 (Intermediate) metric)
UniWorld: Autonomous Driving Pre-training via World Models
In this paper, we draw inspiration from Alberto Elfes' pioneering work in 1989, where he introduced the concept of the occupancy grid as World Models for robots.
Label-less Learning for Traffic Control in an Edge Network
In order to solve this problem, we design a traffic control algorithm based on label-less learning on the edge cloud, which is dubbed as LLTC.
Deep Learning for Multi-View Stereo via Plane Sweep: A Survey
3D reconstruction has lately attracted increasing attention due to its wide application in many areas, such as autonomous driving, robotics and virtual reality.
STS: Surround-view Temporal Stereo for Multi-view 3D Detection
Instead of using a sole monocular depth method, in this work, we propose a novel Surround-view Temporal Stereo (STS) technique that leverages the geometry correspondence between frames across time to facilitate accurate depth learning.
Adaptive Learning for Multi-view Stereo Reconstruction
Deep learning has recently demonstrated its excellent performance on the task of multi-view stereo (MVS).
