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Found 7 papers, 2 papers with code
MV2DFusion: Leveraging Modality-Specific Object Semantics for Multi-Modal 3D Detection
The rise of autonomous vehicles has significantly increased the demand for robust 3D object detection systems.
EASE-DETR: Easing the Competition among Object Queries
This paper views the DETR's non-duplicate detection ability as a competition result among object queries.
Transferring CLIP's Knowledge into Zero-Shot Point Cloud Semantic Segmentation
In this work, we focus on zero-shot point cloud semantic segmentation and propose a simple yet effective baseline to transfer the visual-linguistic knowledge implied in CLIP to point cloud encoder at both feature and output levels.
DriveAdapter: Breaking the Coupling Barrier of Perception and Planning in End-to-End Autonomous Driving
We find that even equipped with a SOTA perception model, directly letting the student model learn the required inputs of the teacher model leads to poor driving performance, which comes from the large distribution gap between predicted privileged inputs and the ground-truth.
Ranked #2 on
Bench2Drive
on Bench2Drive
Sparse Dense Fusion for 3D Object Detection
With the prevalence of multimodal learning, camera-LiDAR fusion has gained popularity in 3D object detection.
Delving into the Devils of Bird's-eye-view Perception: A Review, Evaluation and Recipe
As sensor configurations get more complex, integrating multi-source information from different sensors and representing features in a unified view come of vital importance.
Human-centric Relation Segmentation: Dataset and Solution
For the above exemplar case, our HRS task produces results in the form of relation triplets <girl [left hand], hold, book> and exacts segmentation masks of the book, with which the robot can easily accomplish the grabbing task.
