Automotive radar provides reliable environmental perception in all-weather conditions with affordable cost, but it hardly supplies semantic and geometry information due to the sparsity of detection points. With development automotive technologies recent years, instance segmentation becomes possible by using radar. Its data contain contexts such as cross section micro-Doppler effects, sometimes can provide when field view is obscured. The outcome from could be potentially used input trackers...

As an emerging technology and a relatively affordable device, the 4D imaging radar has already been confirmed effective in performing 3D object detection autonomous driving. Nevertheless, sparsity noisiness of point clouds hinder further performance improvement, in-depth studies about its fusion with other modalities are lacking. On hand, as new image view transformation strategy, "sampling" applied few image-based detectors shown to outperform widely "depth-based splatting" proposed...

The 4D millimeter-Wave (mmWave) radar is a promising technology for vehicle sensing due to its cost-effectiveness and operability in adverse weather conditions. However, the adoption of this has been hindered by sparsity noise issues point cloud data. This article introduces spatial multi-representation fusion (SMURF), novel approach 3D object detection using single imaging radar. SMURF leverages multiple representations points, including pillarization density features multi-dimensional...

As the previous state-of-the-art 4D radar-camera fusion-based 3D object detection method, LXL utilizes predicted image depth distribution maps and radar occupancy grids to assist sampling-based view transformation. However, prediction lacks accuracy consistency, concatenation-based fusion in impedes model robustness. In this work, we propose LXLv2, where modifications are made overcome limitations improve performance. Specifically, considering position error measurements, devise a...

The automotive mmWave radar plays a key role in advanced driver assistance systems (ADAS) and autonomous driving. Deep learning-based instance segmentation enables real-time object identification from the detection points. In conventional training process, accurate annotation is key. However, high-quality annotations of points are challenging to achieve due their ambiguity sparsity. To address this issue, we propose contrastive learning approach for implementing points-based segmentation. We...

Automotive radar provides reliable environmental perception in all-weather conditions with affordable cost, but it hardly supplies semantic and geometry information due to the sparsity of detection points. With developmentof automotive technologies recent years, instance segmentation becomes possible by using radar. Its data contain contexts such as cross section micro-Doppler effects, sometimes can provide when field view is obscured. The outcome from segmentationcould be potentially used...

As an emerging technology and a relatively affordable device, the 4D imaging radar has already been confirmed effective in performing 3D object detection autonomous driving. Nevertheless, sparsity noisiness of point clouds hinder further performance improvement, in-depth studies about its fusion with other modalities are lacking. On hand, as new image view transformation strategy, "sampling" applied few image-based detectors shown to outperform widely "depth-based splatting" proposed...

The 4D Millimeter wave (mmWave) radar is a promising technology for vehicle sensing due to its cost-effectiveness and operability in adverse weather conditions. However, the adoption of this has been hindered by sparsity noise issues point cloud data. This paper introduces spatial multi-representation fusion (SMURF), novel approach 3D object detection using single imaging radar. SMURF leverages multiple representations points, including pillarization density features multi-dimensional...

Millimetre wave (mmWave) radar is a non-intrusive privacy and relatively convenient inexpensive device, which has been demonstrated to be applicable in place of RGB cameras human indoor pose estimation tasks. However, mmWave relies on the collection reflected signals from target, containing information difficult fully applied. This long-standing hindrance improvement accuracy. To address this major challenge, paper introduces probability map guided multi-format feature fusion model,...

Summary We propose a fast wavelet decomposition and reconstruction technique for seismic data based on complex domain matching pursuit algorithm. The is decomposed into wavelets of different control parameters such as central time, amplitude, frequency phase. And certain energy can be selected to reconstruct according geological targets. Following this approach, we remove strong event in enhance the signal effective reservoir fluid information, finally improve prediction hydrocarbon...

Automotive radar provides reliable environmental perception in all-weather conditions with affordable cost, but it hardly supplies semantic and geometry information due to the sparsity of detection points. With development automotive technologies recent years, instance segmentation becomes possible by using radar. Its data contain contexts such as cross section micro-Doppler effects, sometimes can provide when field view is obscured. The outcome from could be potentially used input trackers...