Search Results for author:
Found 12 papers, 3 papers with code
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.
Low-Resolution Self-Attention for Semantic Segmentation
Semantic segmentation tasks naturally require high-resolution information for pixel-wise segmentation and global context information for class prediction.
Construction of Paired Knowledge Graph-Text Datasets Informed by Cyclic Evaluation
Guided by these observations, we construct a new, improved dataset called LAGRANGE using heuristics meant to improve equivalence between KG and text and show the impact of each of the heuristics on cyclic evaluation.
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
PUPS: Point Cloud Unified Panoptic Segmentation
Point cloud panoptic segmentation is a challenging task that seeks a holistic solution for both semantic and instance segmentation to predict groupings of coherent points.
INT: Towards Infinite-frames 3D Detection with An Efficient Framework
By employing INT on CenterPoint, we can get around 7% (Waymo) and 15% (nuScenes) performance boost with only 2~4ms latency overhead, and currently SOTA on the Waymo 3D Detection leaderboard.
Ret3D: Rethinking Object Relations for Efficient 3D Object Detection in Driving Scenes
Current efficient LiDAR-based detection frameworks are lacking in exploiting object relations, which naturally present in both spatial and temporal manners.
BE-STI: Spatial-Temporal Integrated Network for Class-Agnostic Motion Prediction With Bidirectional Enhancement
In this paper, we propose a novel Spatial-Temporal Integrated network with Bidirectional Enhancement, BE-STI, to improve the temporal motion prediction performance by spatial semantic features, which points out an efficient way to combine semantic segmentation and motion prediction.
LIFT: Learning 4D LiDAR Image Fusion Transformer for 3D Object Detection
LiDAR and camera are two common sensors to collect data in time for 3D object detection under the autonomous driving context.
P2T: Pyramid Pooling Transformer for Scene Understanding
A popular solution to this problem is to use a single pooling operation to reduce the sequence length.
Ranked #5 on
RGB Salient Object Detection
on DUTS-TE
(max F-measure metric)
PVGNet: A Bottom-Up One-Stage 3D Object Detector With Integrated Multi-Level Features
Quantization-based methods are widely used in LiDAR points 3D object detection for its efficiency in extracting context information.
