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Found 13 papers, 5 papers with code
PARIS3D: Reasoning-based 3D Part Segmentation Using Large Multimodal Model
We introduce a novel segmentation task known as reasoning part segmentation for 3D objects, aiming to output a segmentation mask based on complex and implicit textual queries about specific parts of a 3D object.
TransRadar: Adaptive-Directional Transformer for Real-Time Multi-View Radar Semantic Segmentation
Scene understanding plays an essential role in enabling autonomous driving and maintaining high standards of performance and safety.
3D Indoor Instance Segmentation in an Open-World
We argue that such a closed-world assumption is restrictive and explore for the first time 3D indoor instance segmentation in an open-world setting, where the model is allowed to distinguish a set of known classes as well as identify an unknown object as unknown and then later incrementally learning the semantic category of the unknown when the corresponding category labels are available.
RadarFormer: Lightweight and Accurate Real-Time Radar Object Detection Model
This improvement was associated with the increasing use of LiDAR sensors and point cloud data to facilitate the task of object detection and recognition in autonomous driving.
Surface-biased Multi-Level Context 3D Object Detection
Self-attention is proven to be effective in encoding correlation information in 3D point clouds by (xie2020mlcvnet).
3D Instance Segmentation via Enhanced Spatial and Semantic Supervision
Specifically, we provide the transformer block with spatial features to facilitate differentiation between similar object queries and incorporate semantic supervision to enhance prediction accuracy based on object class.
CMR3D: Contextualized Multi-Stage Refinement for 3D Object Detection
In this work, we propose Contextualized Multi-Stage Refinement for 3D Object Detection (CMR3D) framework, which takes a 3D scene as input and strives to explicitly integrate useful contextual information of the scene at multiple levels to predict a set of object bounding-boxes along with their corresponding semantic labels.
3D Vision with Transformers: A Survey
The success of the transformer architecture in natural language processing has recently triggered attention in the computer vision field.
On the Robustness of 3D Object Detectors
To the best of our knowledge, we are the first to investigate the robustness of point-based 3D object detectors.
RGB-based Semantic Segmentation Using Self-Supervised Depth Pre-Training
These labels, denoted by HN-labels, represent different height and normal patches, which allow mining of local semantic information that is useful in the task of semantic RGB segmentation.
Ranked #101 on
Semantic Segmentation
on NYU Depth v2
3D Instance Segmentation via Multi-Task Metric Learning
The second goal is to learn instance information by densely estimating directional information of the instance's center of mass for each voxel.
Ranked #2 on
3D Semantic Instance Segmentation
on ScanNetV2
2D-Driven 3D Object Detection in RGB-D Images
We then use the 3D information to orient, place, and score bounding boxes around objects.
Ranked #4 on
Object Detection In Indoor Scenes
on SUN RGB-D
Sim4CV: A Photo-Realistic Simulator for Computer Vision Applications
We present a photo-realistic training and evaluation simulator (Sim4CV) with extensive applications across various fields of computer vision.
