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Found 8 papers, 2 papers with code
V2X-PC: Vehicle-to-everything Collaborative Perception via Point Cluster
The point cluster inherently preserves object information while packing messages, with weak relevance to the collaboration range, and supports explicit structure modeling.
Eliminating Cross-modal Conflicts in BEV Space for LiDAR-Camera 3D Object Detection
Recent 3D object detectors typically utilize multi-sensor data and unify multi-modal features in the shared bird's-eye view (BEV) representation space.
NeuSE: Neural SE(3)-Equivariant Embedding for Consistent Spatial Understanding with Objects
We present NeuSE, a novel Neural SE(3)-Equivariant Embedding for objects, and illustrate how it supports object SLAM for consistent spatial understanding with long-term scene changes.
Object as Query: Lifting any 2D Object Detector to 3D Detection
Existing methods mainly establish 3D representations from multi-view images and adopt a dense detection head for object detection, or employ object queries distributed in 3D space to localize objects.
Robust Change Detection Based on Neural Descriptor Fields
The ability to reason about changes in the environment is crucial for robots operating over extended periods of time.
PlaneSDF-based Change Detection for Long-term Dense Mapping
Given point clouds of the source and target scenes, we propose a three-step PlaneSDF-based change detection approach: (1) PlaneSDF volumes are instantiated within each scene and registered across scenes using plane poses; 2D height maps and object maps are extracted per volume via height projection and connected component analysis.
3D-SPS: Single-Stage 3D Visual Grounding via Referred Point Progressive Selection
3D visual grounding aims to locate the referred target object in 3D point cloud scenes according to a free-form language description.
Improved Pillar with Fine-grained Feature for 3D Object Detection
In contrast, the 2D grid-based methods, such as PointPillar, can easily achieve a stable and efficient speed based on simple 2D convolution, but it is hard to get the competitive accuracy limited by the coarse-grained point clouds representation.
