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Found 8 papers, 6 papers with code
Can 3D Vision-Language Models Truly Understand Natural Language?
To test the language understandability of 3D-VL models, we first propose a language robustness task for systematically assessing 3D-VL models across various tasks, benchmarking their performance when presented with different language style variants.
V-IRL: Grounding Virtual Intelligence in Real Life
There is a sensory gulf between the Earth that humans inhabit and the digital realms in which modern AI agents are created.
Lowis3D: Language-Driven Open-World Instance-Level 3D Scene Understanding
To address this challenge, we propose to harness pre-trained vision-language (VL) foundation models that encode extensive knowledge from image-text pairs to generate captions for multi-view images of 3D scenes.
Ranked #3 on
3D Open-Vocabulary Instance Segmentation
on S3DIS
3D Open-Vocabulary Instance Segmentation
Instance Segmentation
+4
RegionPLC: Regional Point-Language Contrastive Learning for Open-World 3D Scene Understanding
Existing 3D scene understanding tasks have achieved high performance on close-set benchmarks but fail to handle novel categories in real-world applications.
PLA: Language-Driven Open-Vocabulary 3D Scene Understanding
Open-vocabulary scene understanding aims to localize and recognize unseen categories beyond the annotated label space.
Ranked #2 on
3D Open-Vocabulary Instance Segmentation
on S3DIS
3D Open-Vocabulary Instance Segmentation
Contrastive Learning
+4
Towards Efficient 3D Object Detection with Knowledge Distillation
Then, we build a benchmark to assess existing KD methods developed in the 2D domain for 3D object detection upon six well-constructed teacher-student pairs.
DODA: Data-oriented Sim-to-Real Domain Adaptation for 3D Semantic Segmentation
Deep learning approaches achieve prominent success in 3D semantic segmentation.
PAConv: Position Adaptive Convolution with Dynamic Kernel Assembling on Point Clouds
The key of PAConv is to construct the convolution kernel by dynamically assembling basic weight matrices stored in Weight Bank, where the coefficients of these weight matrices are self-adaptively learned from point positions through ScoreNet.
Ranked #2 on
Point Cloud Segmentation
on PointCloud-C
