37 citations · 137 across the 14 of their papers we have counts for
15 papers
From Contrast to Consistency: Rethinking Event-based Continuous-Time Optical Flow Estimation
Rui Hu, Song Wu, Wen Yang +1
Estimating continuous optical flow is a fundamental yet challenging problem in dynamic visual perception. Event-based cameras, with microsecond latency and high dynamic range, capt…
Reconstructing Objects in-the-wild for Realistic Sensor Simulation
Ze Yang, Sivabalan Manivasagam, Yun Chen +3
Reconstructing objects from real world data and rendering them at novel views is critical to bringing realism, diversity and scale to simulation for robotics training and testing.…
Copilot4D: Learning Unsupervised World Models for Autonomous Driving via Discrete Diffusion
Lunjun Zhang, Yuwen Xiong, Ze Yang +3
Learning world models can teach an agent how the world works in an unsupervised manner. Even though it can be viewed as a special case of sequence modeling, progress for scaling wo…
Rethinking Closed-loop Training for Autonomous Driving
Chris Zhang, Runsheng Guo, Wenyuan Zeng +5
Recent advances in high-fidelity simulators have enabled closed-loop training of autonomous driving agents, potentially solving the distribution shift in training v.s. deployment a…
PLUMENet: Efficient 3D Object Detection from Stereo Images
Yan Wang, Bin Yang, Rui Hu +2
3D object detection is a key component of many robotic applications such as self-driving vehicles. While many approaches rely on expensive 3D sensors such as LiDAR to produce accur…
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…
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…
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…
Learning Lane Graph Representations for Motion Forecasting
Ming Liang, Bin Yang, Rui Hu +4
We propose a motion forecasting model that exploits a novel structured map representation as well as actor-map interactions. Instead of encoding vectorized maps as raster images, w…
PnPNet: End-to-End Perception and Prediction with Tracking in the Loop
Ming Liang, Bin Yang, Wenyuan Zeng +4
We tackle the problem of joint perception and motion forecasting in the context of self-driving vehicles. Towards this goal we propose PnPNet, an end-to-end model that takes as inp…
MultiXNet: Multiclass Multistage Multimodal Motion Prediction
Nemanja Djuric, Henggang Cui, Zhaoen Su +13
One of the critical pieces of the self-driving puzzle is understanding the surroundings of a self-driving vehicle (SDV) and predicting how these surroundings will change in the nea…
PolyTransform: Deep Polygon Transformer for Instance Segmentation
Justin Liang, Namdar Homayounfar, Wei-Chiu Ma +3
In this paper, we propose PolyTransform, a novel instance segmentation algorithm that produces precise, geometry-preserving masks by combining the strengths of prevailing segmentat…
DeepPruner: Learning Efficient Stereo Matching via Differentiable PatchMatch
Shivam Duggal, Shenlong Wang, Wei-Chiu Ma +2
Our goal is to significantly speed up the runtime of current state-of-the-art stereo algorithms to enable real-time inference. Towards this goal, we developed a differentiable Patc…
Deep Rigid Instance Scene Flow
Wei-Chiu Ma, Shenlong Wang, Rui Hu +2
In this paper we tackle the problem of scene flow estimation in the context of self-driving. We leverage deep learning techniques as well as strong priors as in our application dom…
UPSNet: A Unified Panoptic Segmentation Network
Yuwen Xiong, Renjie Liao, Hengshuang Zhao +4
In this paper, we propose a unified panoptic segmentation network (UPSNet) for tackling the newly proposed panoptic segmentation task. On top of a single backbone residual network,…