Search Results for author:
Found 17 papers, 13 papers with code
ARTiST: Automated Text Simplification for Task Guidance in Augmented Reality
Text presented in augmented reality provides in-situ, real-time information for users.
VADv2: End-to-End Vectorized Autonomous Driving via Probabilistic Planning
Learning a human-like driving policy from large-scale driving demonstrations is promising, but the uncertainty and non-deterministic nature of planning make it challenging.
MapTRv2: An End-to-End Framework for Online Vectorized HD Map Construction
We propose a unified permutation-equivalent modeling approach, \ie, modeling map element as a point set with a group of equivalent permutations, which accurately describes the shape of map element and stabilizes the learning process.
VMA: Divide-and-Conquer Vectorized Map Annotation System for Large-Scale Driving Scene
We design a divide-and-conquer annotation scheme to solve the spatial extensibility problem of HD map generation, and abstract map elements with a variety of geometric patterns as unified point sequence representation, which can be extended to most map elements in the driving scene.
TinyDet: Accurate Small Object Detection in Lightweight Generic Detectors
Small object detection requires the detection head to scan a large number of positions on image feature maps, which is extremely hard for computation- and energy-efficient lightweight generic detectors.
VAD: Vectorized Scene Representation for Efficient Autonomous Driving
In this paper, we propose VAD, an end-to-end vectorized paradigm for autonomous driving, which models the driving scene as a fully vectorized representation.
Lane Graph as Path: Continuity-preserving Path-wise Modeling for Online Lane Graph Construction
We present a path-based online lane graph construction method, termed LaneGAP, which end-to-end learns the path and recovers the lane graph via a Path2Graph algorithm.
Perceive, Interact, Predict: Learning Dynamic and Static Clues for End-to-End Motion Prediction
Motion prediction is highly relevant to the perception of dynamic objects and static map elements in the scenarios of autonomous driving.
BoxTeacher: Exploring High-Quality Pseudo Labels for Weakly Supervised Instance Segmentation
Most existing methods for weakly supervised instance segmentation focus on designing heuristic losses with priors from bounding boxes.
MapTR: Structured Modeling and Learning for Online Vectorized HD Map Construction
High-definition (HD) map provides abundant and precise environmental information of the driving scene, serving as a fundamental and indispensable component for planning in autonomous driving system.
Ranked #7 on
3D Lane Detection
on OpenLane-V2 val
Vision-based Uneven BEV Representation Learning with Polar Rasterization and Surface Estimation
In this work, we propose PolarBEV for vision-based uneven BEV representation learning.
Polar Parametrization for Vision-based Surround-View 3D Detection
Based on Polar Parametrization, we propose a surround-view 3D DEtection TRansformer, named PolarDETR.
Featurized Query R-CNN
The query mechanism introduced in the DETR method is changing the paradigm of object detection and recently there are many query-based methods have obtained strong object detection performance.
Efficient and Robust 2D-to-BEV Representation Learning via Geometry-guided Kernel Transformer
GKT leverages the geometric priors to guide the transformer to focus on discriminative regions and unfolds kernel features to generate BEV representation.
AziNorm: Exploiting the Radial Symmetry of Point Cloud for Azimuth-Normalized 3D Perception
For segmentation, we integrate AziNorm into KPConv.
Sparse Instance Activation for Real-Time Instance Segmentation
In this paper, we propose a conceptually novel, efficient, and fully convolutional framework for real-time instance segmentation.
Ranked #8 on
Real-time Instance Segmentation
on MSCOCO
Hierarchical Aggregation for 3D Instance Segmentation
Instance segmentation on point clouds is a fundamental task in 3D scene perception.
Ranked #4 on
3D Instance Segmentation
on S3DIS
(mCov metric, using extra
training data)
