841 citations · 7.7k across the 158 of their papers we have counts for
41 papers · 1 filter
Learning Joint 2D-3D Representations for Depth Completion
Yun Chen, Bin Yang, Ming Liang +1
In this paper, we tackle the problem of depth completion from RGBD data. Towards this goal, we design a simple yet effective neural network block that learns to extract joint 2D an…
Multi-Task Multi-Sensor Fusion for 3D Object Detection
Ming Liang, Bin Yang, Yun Chen +2
In this paper we propose to exploit multiple related tasks for accurate multi-sensor 3D object detection. Towards this goal we present an end-to-end learnable architecture that rea…
Fast and Furious: Real Time End-to-End 3D Detection, Tracking and Motion Forecasting with a Single Convolutional Net
Wenjie Luo, Bin Yang, Raquel Urtasun
In this paper we propose a novel deep neural network that is able to jointly reason about 3D detection, tracking and motion forecasting given data captured by a 3D sensor. By joint…
DAGMapper: Learning to Map by Discovering Lane Topology
Namdar Homayounfar, Wei-Chiu Ma, Justin Liang +3
One of the fundamental challenges to scale self-driving is being able to create accurate high definition maps (HD maps) with low cost. Current attempts to automate this process typ…
Hierarchical Recurrent Attention Networks for Structured Online Maps
Namdar Homayounfar, Wei-Chiu Ma, Shrinidhi Kowshika Lakshmikanth +1
In this paper, we tackle the problem of online road network extraction from sparse 3D point clouds. Our method is inspired by how an annotator builds a lane graph, by first identif…
Convolutional Recurrent Network for Road Boundary Extraction
Justin Liang, Namdar Homayounfar, Wei-Chiu Ma +2
Creating high definition maps that contain precise information of static elements of the scene is of utmost importance for enabling self driving cars to drive safely. In this paper…
HDNET: Exploiting HD Maps for 3D Object Detection
Bin Yang, Ming Liang, Raquel Urtasun
In this paper we show that High-Definition (HD) maps provide strong priors that can boost the performance and robustness of modern 3D object detectors. Towards this goal, we design…
Deep Continuous Fusion for Multi-Sensor 3D Object Detection
Ming Liang, Bin Yang, Shenlong Wang +1
In this paper, we propose a novel 3D object detector that can exploit both LIDAR as well as cameras to perform very accurate localization. Towards this goal, we design an end-to-en…
Learning to Localize Through Compressed Binary Maps
Xinkai Wei, Ioan Andrei Bârsan, Shenlong Wang +2
One of the main difficulties of scaling current localization systems to large environments is the on-board storage required for the maps. In this paper we propose to learn to compr…
Learning to Localize Using a LiDAR Intensity Map
Ioan Andrei Bârsan, Shenlong Wang, Andrei Pokrovsky +1
In this paper we propose a real-time, calibration-agnostic and effective localization system for self-driving cars. Our method learns to embed the online LiDAR sweeps and intensity…
A PAC-Bayesian Approach to Generalization Bounds for Graph Neural Networks
Renjie Liao, Raquel Urtasun, Richard Zemel
In this paper, we derive generalization bounds for the two primary classes of graph neural networks (GNNs), namely graph convolutional networks (GCNs) and message passing GNNs (MPG…
GeoNet++: Iterative Geometric Neural Network with Edge-Aware Refinement for Joint Depth and Surface Normal Estimation
Xiaojuan Qi, Zhengzhe Liu, Renjie Liao +3
In this paper, we propose a geometric neural network with edge-aware refinement (GeoNet++) to jointly predict both depth and surface normal maps from a single image. Building on to…
Pit30M: A Benchmark for Global Localization in the Age of Self-Driving Cars
Julieta Martinez, Sasha Doubov, Jack Fan +4
We are interested in understanding whether retrieval-based localization approaches are good enough in the context of self-driving vehicles. Towards this goal, we introduce Pit30M,…
Recovering and Simulating Pedestrians in the Wild
Ze Yang, Siva Manivasagam, Ming Liang +3
Sensor simulation is a key component for testing the performance of self-driving vehicles and for data augmentation to better train perception systems. Typical approaches rely on a…
StrObe: Streaming Object Detection from LiDAR Packets
Davi Frossard, Simon Suo, Sergio Casas +3
Many modern robotics systems employ LiDAR as their main sensing modality due to its geometrical richness. Rolling shutter LiDARs are particularly common, in which an array of laser…
Universal Embeddings for Spatio-Temporal Tagging of Self-Driving Logs
Sean Segal, Eric Kee, Wenjie Luo +3
In this paper, we tackle the problem of spatio-temporal tagging of self-driving scenes from raw sensor data. Our approach learns a universal embedding for all tags, enabling effici…
LiRaNet: End-to-End Trajectory Prediction using Spatio-Temporal Radar Fusion
Meet Shah, Zhiling Huang, Ankit Laddha +5
In this paper, we present LiRaNet, a novel end-to-end trajectory prediction method which utilizes radar sensor information along with widely used lidar and high definition (HD) map…
Learning to Communicate and Correct Pose Errors
Nicholas Vadivelu, Mengye Ren, James Tu +2
Learned communication makes multi-agent systems more effective by aggregating distributed information. However, it also exposes individual agents to the threat of erroneous message…
MuSCLE: Multi Sweep Compression of LiDAR using Deep Entropy Models
Sourav Biswas, Jerry Liu, Kelvin Wong +2
We present a novel compression algorithm for reducing the storage of LiDAR sensor data streams. Our model exploits spatio-temporal relationships across multiple LiDAR sweeps to red…
Perceive, Attend, and Drive: Learning Spatial Attention for Safe Self-Driving
Bob Wei, Mengye Ren, Wenyuan Zeng +3
In this paper, we propose an end-to-end self-driving network featuring a sparse attention module that learns to automatically attend to important regions of the input. The attentio…
Permute, Quantize, and Fine-tune: Efficient Compression of Neural Networks
Julieta Martinez, Jashan Shewakramani, Ting Wei Liu +3
Compressing large neural networks is an important step for their deployment in resource-constrained computational platforms. In this context, vector quantization is an appealing fr…
Conditional Entropy Coding for Efficient Video Compression
Jerry Liu, Shenlong Wang, Wei-Chiu Ma +4
We propose a very simple and efficient video compression framework that only focuses on modeling the conditional entropy between frames. Unlike prior learning-based approaches, we…
Weakly-supervised 3D Shape Completion in the Wild
Jiayuan Gu, Wei-Chiu Ma, Sivabalan Manivasagam +5
3D shape completion for real data is important but challenging, since partial point clouds acquired by real-world sensors are usually sparse, noisy and unaligned. Different from pr…
V2VNet: Vehicle-to-Vehicle Communication for Joint Perception and Prediction
Tsun-Hsuan Wang, Sivabalan Manivasagam, Ming Liang +4
In this paper, we explore the use of vehicle-to-vehicle (V2V) communication to improve the perception and motion forecasting performance of self-driving vehicles. By intelligently…