22 accepted papers
Recent multimodal fusion methods, integrating images with LiDAR point clouds, have shown promise in scene flow estimation. However, the fusion of 4D millimeter wave radar and LiDAR remains unexplored. Unlike LiDAR, radar is cheaper, more robust in various weather conditions and can detect point-wise
LiDAR Semantic Scene Completion (SSC) in autonomous driving requires predicting both dense occupancy and semantic labels from sparse input point cloud. Existing methods typically adopt cascaded architecture for feature dilation and semantic abstraction, which blurs distinctive geometric patterns and
Due to the difficulty of obtaining ground-truth data for 4D radar scene flow estimation, previous methods typically rely on either self-supervised losses or cross-modal supervision using 3D LiDAR data, 2D images, and odometry. However, self-supervised approaches often yield suboptimal results due to…
Vehicle-to-everything (V2X) collaborative perception has recently gained increasing attention in autonomous driving due to its ability to enhance scene understanding by integrating information from other collaborators, e.g. vehicles or infrastructure. Existing algorithms usually share deep features…
Collaborative visual perception methods have gained widespread attention in the autonomous driving community in recent years due to their ability to address sensor limitation problems. However, the absence of explicit depth information often makes it difficult for camera-based perception systems, e.…
3D object detection is one of the fundamental perception tasks for autonomous vehicles. Fulfilling such a task with a 4D millimeter-wave radar is very attractive since the sensor is able to acquire 3D point clouds similar to Lidar while maintaining robust measurements under adverse weather. However,…
Despite the great success of deep learning in stereo matching recovering accurate disparity maps is still challenging. Currently L1 and cross-entropy are the two most widely used losses for stereo network training. Compared with the former the latter usually performs better thanks to its probability…
The performance of neural radiance field technologies deteriorates rapidly when sparse views are used as input. In this paper, we propose a simulated viewpoint enhancement for surface reconstruction that extracts diverse geometric features from the depth to address this limitation. We design a novel
"Multi-agent collaborative perception has emerged as a widely recognized technology in the field of autonomous driving in recent years. However, current collaborative perception predominantly relies on LiDAR point clouds, with significantly less attention given to methods using camera images. This s…
"Despite the progress on 3D point cloud deep learning, most prior works focus on learning features that are invariant to translation and point permutation, and very limited efforts have been devoted for rotation invariant property. Several recent studies achieve rotation invariance at the cost of lo…
Stereo vision is an essential component for lots of robot vision applications. The performance of stereo matching has been greatly improved recently thanks to deep learning technologies. However, most of the current deep networks directly use naive disparity loss to train the network, regardless of
As a low-level task of 3D perception, scene flow is a fundamental representation of dynamic scenes and provides non-rigid motion descriptions for the objects in the 3D environment, which can strongly support many upper-level applications. Inspired by the revolutionary success of deep learning, many
Visual relocalization aims to estimate the absolute camera pose from an image or sequential images. Recent works tackle this problem by exploiting deep neural networks to regress camera poses. However, spatial and temporal clues from sequential images still remain underexplored, resulting in inaccur
In recent years 3D object detection from LiDAR point clouds has made great progress thanks to the development of deep learning technologies. Although voxel or point based methods are popular in 3D object detection, they usually involve time-consuming operations such as 3D convolutions on voxels or b…
Most self-supervised monocular visual odometry (VO) suffer from the scale ambiguity problem. A promising way to address this problem is to introduce additional information for training. In this work, we propose a new depth privileged framework to learn a monocular VO. It assumes that sparse depth is
Monocular 3D object detection is an essential task in autonomous driving. However, most current methods consider each 3D object in the scene as an independent training sample, while ignoring their inherent geometric relations, thus inevitably resulting in a lack of leveraging spatial constraints. In…
Depth Completion can produce a dense depth map from a sparse input and provide a more complete 3D description of the environment. Despite great progress made in depth completion, the sparsity of the input and low density of the ground truth still make this problem challenging. In this work, we propo
Although instance segmentation has made considerable advancement over recent years, it's still a challenge to design high accuracy algorithms with real-time performance. In this paper, we propose a real-time instance segmentation framework termed OrienMask. Upon the one-stage object detector YOLOv3,…
Along with point features, line features play an important role in achieving robust Simultaneous Localization and Mapping (SLAM) under complex environments. This paper proposes a fast and effective method, namely Superline, to simultaneously detect line segments and generate robust descriptors for m…
Lane mark detection is one of the key tasks for autonomous driving systems. Accurate detection of lane marks under complex urban environments remains a challenge. In this paper, an end-to-end lane mark detection network named DSSF-net, which is capable of directly outputting the accurate fitted lane…
Omnidirectional catadioptric systems are popular in robotic applications thanks to their large field of view. For 3D scene reconstruction in a single shot, usually two different catadioptric cameras are needed. More cameras may contribute to better reconstruction while larger mounting space and high…
Robust localization is a prerequisite for autonomous vehicles. Traditional visual localization methods like visual odometry suffer error accumulation on long range navigation. In this paper, a flexible road map based probabilistic filtering method is proposed to tackle this problem. To effectively m…