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Found 16 papers, 3 papers with code
Multi-modal 3D Human Pose Estimation with 2D Weak Supervision in Autonomous Driving
3D human pose estimation (HPE) in autonomous vehicles (AV) differs from other use cases in many factors, including the 3D resolution and range of data, absence of dense depth maps, failure modes for LiDAR, relative location between the camera and LiDAR, and a high bar for estimation accuracy.
Revisiting 3D Object Detection From an Egocentric Perspective
Given the insight that SDE would benefit from more accurate geometry descriptions, we propose to represent objects as amodal contours, specifically amodal star-shaped polygons, and devise a simple model, StarPoly, to predict such contours.
Lidar Range Image Compression with Deep Delta Encoding
However, most prior work focus on the generic point cloud representation, neglecting the spatial patterns of the points from lidar range images.
SPG: Unsupervised Domain Adaptation for 3D Object Detection via Semantic Point Generation
On the Waymo Open Dataset and KITTI, SPG improves 3D detection results of these two methods across all categories.
Ranked #2 on
3D Object Detection
on KITTI Cars Hard
Large Scale Interactive Motion Forecasting for Autonomous Driving : The Waymo Open Motion Dataset
Furthermore, we introduce a new set of metrics that provides a comprehensive evaluation of both single agent and joint agent interaction motion forecasting models.
3D-MAN: 3D Multi-frame Attention Network for Object Detection
In this paper, we present 3D-MAN: a 3D multi-frame attention network that effectively aggregates features from multiple perspectives and achieves state-of-the-art performance on Waymo Open Dataset.
Offboard 3D Object Detection from Point Cloud Sequences
While current 3D object recognition research mostly focuses on the real-time, onboard scenario, there are many offboard use cases of perception that are largely under-explored, such as using machines to automatically generate high-quality 3D labels.
Large Scale Interactive Motion Forecasting for Autonomous Driving: The Waymo Open Motion Dataset
Furthermore, we introduce a new set of metrics that provides a comprehensive evaluation of both single agent and joint agent interaction motion forecasting models.
SurfelGAN: Synthesizing Realistic Sensor Data for Autonomous Driving
In such scenarios, the ability to accurately simulate the vehicle sensors such as cameras, lidar or radar is essential.
Scalability in Perception for Autonomous Driving: Waymo Open Dataset
In an effort to help align the research community's contributions with real-world self-driving problems, we introduce a new large scale, high quality, diverse dataset.
End-to-End Multi-View Fusion for 3D Object Detection in LiDAR Point Clouds
In this paper, we aim to synergize the birds-eye view and the perspective view and propose a novel end-to-end multi-view fusion (MVF) algorithm, which can effectively learn to utilize the complementary information from both.
StarNet: Targeted Computation for Object Detection in Point Clouds
We show how our redesign---namely using only local information and using sampling instead of learned proposals---leads to a significantly more flexible and adaptable system: we demonstrate how we can vary the computational cost of a single trained StarNet without retraining, and how we can target proposals towards areas of interest with priors and heuristics.
MVX-Net: Multimodal VoxelNet for 3D Object Detection
Many recent works on 3D object detection have focused on designing neural network architectures that can consume point cloud data.
VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection
Accurate detection of objects in 3D point clouds is a central problem in many applications, such as autonomous navigation, housekeeping robots, and augmented/virtual reality.
Ranked #1 on
Object Localization
on KITTI Cars Hard
Classification of Histology Sections via Multispectral Convolutional Sparse Coding
Image-based classification of histology sections plays an important role in predicting clinical outcomes.
