229 citations · 351 across the 12 of their papers we have counts for
10 papers · 1 filter
Superpixel Transformers for Efficient Semantic Segmentation
Alex Zihao Zhu, Jieru Mei, Siyuan Qiao +4
Semantic segmentation, which aims to classify every pixel in an image, is a key task in machine perception, with many applications across robotics and autonomous driving. Due to th…
CramNet: Camera-Radar Fusion with Ray-Constrained Cross-Attention for Robust 3D Object Detection
Jyh-Jing Hwang, Henrik Kretzschmar, Joshua Manela +4
Robust 3D object detection is critical for safe autonomous driving. Camera and radar sensors are synergistic as they capture complementary information and work well under different…
Instance Segmentation with Cross-Modal Consistency
Alex Zihao Zhu, Vincent Casser, Reza Mahjourian +2
Segmenting object instances is a key task in machine perception, with safety-critical applications in robotics and autonomous driving. We introduce a novel approach to instance seg…
Waymo Open Dataset: Panoramic Video Panoptic Segmentation
Jieru Mei, Alex Zihao Zhu, Xinchen Yan +6
Panoptic image segmentation is the computer vision task of finding groups of pixels in an image and assigning semantic classes and object instance identifiers to them. Research in…
Depth Estimation Matters Most: Improving Per-Object Depth Estimation for Monocular 3D Detection and Tracking
Longlong Jing, Ruichi Yu, Henrik Kretzschmar +11
Monocular image-based 3D perception has become an active research area in recent years owing to its applications in autonomous driving. Approaches to monocular 3D perception includ…
LET-3D-AP: Longitudinal Error Tolerant 3D Average Precision for Camera-Only 3D Detection
Wei-Chih Hung, Vincent Casser, Henrik Kretzschmar +2
The 3D Average Precision (3D AP) relies on the intersection over union between predictions and ground truth objects. However, camera-only detectors have limited depth accuracy, whi…
Block-NeRF: Scalable Large Scene Neural View Synthesis
Matthew Tancik, Vincent Casser, Xinchen Yan +5
We present Block-NeRF, a variant of Neural Radiance Fields that can represent large-scale environments. Specifically, we demonstrate that when scaling NeRF to render city-scale sce…
SoDA: Multi-Object Tracking with Soft Data Association
Wei-Chih Hung, Henrik Kretzschmar, Tsung-Yi Lin +4
Robust multi-object tracking (MOT) is a prerequisite fora safe deployment of self-driving cars. Tracking objects, however, remains a highly challenging problem, especially in clutt…
SurfelGAN: Synthesizing Realistic Sensor Data for Autonomous Driving
Zhenpei Yang, Yuning Chai, Dragomir Anguelov +5
Autonomous driving system development is critically dependent on the ability to replay complex and diverse traffic scenarios in simulation. In such scenarios, the ability to accura…
Scalability in Perception for Autonomous Driving: Waymo Open Dataset
Pei Sun, Henrik Kretzschmar, Xerxes Dotiwalla +22
The research community has increasing interest in autonomous driving research, despite the resource intensity of obtaining representative real world data. Existing self-driving dat…