Search Results for author:
Found 6 papers, 3 papers with code
Agent3D-Zero: An Agent for Zero-shot 3D Understanding
The ability to understand and reason the 3D real world is a crucial milestone towards artificial general intelligence.
HVDistill: Transferring Knowledge from Images to Point Clouds via Unsupervised Hybrid-View Distillation
We present a hybrid-view-based knowledge distillation framework, termed HVDistill, to guide the feature learning of a point cloud neural network with a pre-trained image network in an unsupervised man- ner.
PoIFusion: Multi-Modal 3D Object Detection via Fusion at Points of Interest
In this work, we present PoIFusion, a simple yet effective multi-modal 3D object detection framework to fuse the information of RGB images and LiDAR point clouds at the point of interest (abbreviated as PoI).
PonderV2: Pave the Way for 3D Foundation Model with A Universal Pre-training Paradigm
In this paper, we introduce a novel universal 3D pre-training framework designed to facilitate the acquisition of efficient 3D representation, thereby establishing a pathway to 3D foundational models.
Ranked #1 on
3D Semantic Segmentation
on ScanNet++
(using extra training data)
UniPAD: A Universal Pre-training Paradigm for Autonomous Driving
In the context of autonomous driving, the significance of effective feature learning is widely acknowledged.
TrajMatch: Towards Automatic Spatio-temporal Calibration for Roadside LiDARs through Trajectory Matching
In this paper, we propose TrajMatch -- the first system that can automatically calibrate for roadside LiDARs in both time and space.
