42 citations · 120 across the 20 of their papers we have counts for
20 papers
GPA-3D: Geometry-aware Prototype Alignment for Unsupervised Domain Adaptive 3D Object Detection from Point Clouds
Ziyu Li, Jingming Guo, Tongtong Cao +2
LiDAR-based 3D detection has made great progress in recent years. However, the performance of 3D detectors is considerably limited when deployed in unseen environments, owing to th…
Improving LiDAR 3D Object Detection via Range-based Point Cloud Density Optimization
Eduardo R. Corral-Soto, Alaap Grandhi, Yannis Y. He +2
In recent years, much progress has been made in LiDAR-based 3D object detection mainly due to advances in detector architecture designs and availability of large-scale LiDAR datase…
NeRFVS: Neural Radiance Fields for Free View Synthesis via Geometry Scaffolds
Chen Yang, Peihao Li, Zanwei Zhou +5
We present NeRFVS, a novel neural radiance fields (NeRF) based method to enable free navigation in a room. NeRF achieves impressive performance in rendering images for novel views…
AOP-Net: All-in-One Perception Network for Joint LiDAR-based 3D Object Detection and Panoptic Segmentation
Yixuan Xu, Hamidreza Fazlali, Yuan Ren +1
LiDAR-based 3D object detection and panoptic segmentation are two crucial tasks in the perception systems of autonomous vehicles and robots. In this paper, we propose All-in-One Pe…
PCGen: Point Cloud Generator for LiDAR Simulation
Chenqi Li, Yuan Ren, Bingbing Liu
Data is a fundamental building block for LiDAR perception systems. Unfortunately, real-world data collection and annotation is extremely costly & laborious. Recently, real data bas…
Domain Adaptation in 3D Object Detection with Gradual Batch Alternation Training
Mrigank Rochan, Xingxin Chen, Alaap Grandhi +2
We consider the problem of domain adaptation in LiDAR-based 3D object detection. Towards this, we propose a simple yet effective training strategy called Gradual Batch Alternation…
A Versatile Multi-View Framework for LiDAR-based 3D Object Detection with Guidance from Panoptic Segmentation
Hamidreza Fazlali, Yixuan Xu, Yuan Ren +1
3D object detection using LiDAR data is an indispensable component for autonomous driving systems. Yet, only a few LiDAR-based 3D object detection methods leverage segmentation inf…
CPSeg: Cluster-free Panoptic Segmentation of 3D LiDAR Point Clouds
Enxu Li, Ryan Razani, Yixuan Xu +1
A fast and accurate panoptic segmentation system for LiDAR point clouds is crucial for autonomous driving vehicles to understand the surrounding objects and scenes. Existing approa…
How to Build a Curb Dataset with LiDAR Data for Autonomous Driving
Dongfeng Bai, Tongtong Cao, Jingming Guo +1
Curbs are one of the essential elements of urban and highway traffic environments. Robust curb detection provides road structure information for motion planning in an autonomous dr…
GP-S3Net: Graph-based Panoptic Sparse Semantic Segmentation Network
Ryan Razani, Ran Cheng, Enxu Li +3
Panoptic segmentation as an integrated task of both static environmental understanding and dynamic object identification, has recently begun to receive broad research interest. In…
Unsupervised Domain Adaptation in LiDAR Semantic Segmentation with Self-Supervision and Gated Adapters
Mrigank Rochan, Shubhra Aich, Eduardo R. Corral-Soto +2
In this paper, we focus on a less explored, but more realistic and complex problem of domain adaptation in LiDAR semantic segmentation. There is a significant drop in performance o…
Lite-HDSeg: LiDAR Semantic Segmentation Using Lite Harmonic Dense Convolutions
Ryan Razani, Ran Cheng, Ehsan Taghavi +1
Autonomous driving vehicles and robotic systems rely on accurate perception of their surroundings. Scene understanding is one of the crucial components of perception modules. Among…
S3Net: 3D LiDAR Sparse Semantic Segmentation Network
Ran Cheng, Ryan Razani, Yuan Ren +1
Semantic Segmentation is a crucial component in the perception systems of many applications, such as robotics and autonomous driving that rely on accurate environmental perception…
(AF)2-S3Net: Attentive Feature Fusion with Adaptive Feature Selection for Sparse Semantic Segmentation Network
Ran Cheng, Ryan Razani, Ehsan Taghavi +2
Autonomous robotic systems and self driving cars rely on accurate perception of their surroundings as the safety of the passengers and pedestrians is the top priority. Semantic seg…
S3CNet: A Sparse Semantic Scene Completion Network for LiDAR Point Clouds
Ran Cheng, Christopher Agia, Yuan Ren +2
With the increasing reliance of self-driving and similar robotic systems on robust 3D vision, the processing of LiDAR scans with deep convolutional neural networks has become a tre…
Bidirectional Attention Network for Monocular Depth Estimation
Shubhra Aich, Jean Marie Uwabeza Vianney, Md Amirul Islam +2
In this paper, we propose a Bidirectional Attention Network (BANet), an end-to-end framework for monocular depth estimation (MDE) that addresses the limitation of effectively integ…
TORNADO-Net: mulTiview tOtal vaRiatioN semAntic segmentation with Diamond inceptiOn module
Martin Gerdzhev, Ryan Razani, Ehsan Taghavi +1
Semantic segmentation of point clouds is a key component of scene understanding for robotics and autonomous driving. In this paper, we introduce TORNADO-Net - a neural network for…
RefinedMPL: Refined Monocular PseudoLiDAR for 3D Object Detection in Autonomous Driving
Jean Marie Uwabeza Vianney, Shubhra Aich, Bingbing Liu
In this paper, we strive for solving the ambiguities arisen by the astoundingly high density of raw PseudoLiDAR for monocular 3D object detection for autonomous driving. Without mu…
BoxNet: A Deep Learning Method for 2D Bounding Box Estimation from Bird's-Eye View Point Cloud
Ehsan Nezhadarya, Yang Liu, Bingbing Liu
We present a learning-based method to estimate the object bounding box from its 2D bird's-eye view (BEV) LiDAR points. Our method, entitled BoxNet, exploits a simple deep neural ne…
Adaptive Hierarchical Down-Sampling for Point Cloud Classification
Ehsan Nezhadarya, Ehsan Taghavi, Ryan Razani +2
While several convolution-like operators have recently been proposed for extracting features out of point clouds, down-sampling an unordered point cloud in a deep neural network ha…