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Found 10 papers, 6 papers with code
Not All Voxels Are Equal: Hardness-Aware Semantic Scene Completion with Self-Distillation
Furthermore, the voxels in the boundary region are more challenging to differentiate than those in the interior.
MGMap: Mask-Guided Learning for Online Vectorized HD Map Construction
Currently, high-definition (HD) map construction leans towards a lightweight online generation tendency, which aims to preserve timely and reliable road scene information.
LVIC: Multi-modality segmentation by Lifting Visual Info as Cue
In our experiments, we find that this projection error is the devil in point painting.
Ranked #1 on
3D Object Detection
on ScanNetV2
PeP: a Point enhanced Painting method for unified point cloud tasks
In our work, we proposed a novel PeP module to tackle above issue.
HuBo-VLM: Unified Vision-Language Model designed for HUman roBOt interaction tasks
Human robot interaction is an exciting task, which aimed to guide robots following instructions from human.
Ranked #1 on
Referring Expression Comprehension
on Talk2Car
FusionAD: Multi-modality Fusion for Prediction and Planning Tasks of Autonomous Driving
Building a multi-modality multi-task neural network toward accurate and robust performance is a de-facto standard in perception task of autonomous driving.
OG: Equip vision occupancy with instance segmentation and visual grounding
Occupancy prediction tasks focus on the inference of both geometry and semantic labels for each voxel, which is an important perception mission.
READ: Large-Scale Neural Scene Rendering for Autonomous Driving
In this paper, a large-scale neural rendering method is proposed to synthesize the autonomous driving scene~(READ), which makes it possible to synthesize large-scale driving scenarios on a PC through a variety of sampling schemes.
Ranked #1 on
Novel View Synthesis
on KITTI
LTP: Lane-Based Trajectory Prediction for Autonomous Driving
Experiments on the Argoverse dataset show that the proposed method outperforms state-of-the-art methods, and the lane segments-based proposals as well as the variance-based non-maximum suppression strategy both contribute to the performance improvement.
Domain Generalization for Vision-based Driving Trajectory Generation
In this paper, we propose a domain generalization method for vision-based driving trajectory generation for autonomous vehicles in urban environments, which can be seen as a solution to extend the Invariant Risk Minimization (IRM) method in complex problems.
