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Found 11 papers, 9 papers with code
SAM2Point: Segment Any 3D as Videos in Zero-shot and Promptable Manners
We introduce SAM2Point, a preliminary exploration adapting Segment Anything Model 2 (SAM 2) for zero-shot and promptable 3D segmentation.
Lumina-Next: Making Lumina-T2X Stronger and Faster with Next-DiT
Lumina-T2X is a nascent family of Flow-based Large Diffusion Transformers that establishes a unified framework for transforming noise into various modalities, such as images and videos, conditioned on text instructions.
No Time to Train: Empowering Non-Parametric Networks for Few-shot 3D Scene Segmentation
To reduce the reliance on large-scale datasets, recent works in 3D segmentation resort to few-shot learning.
Point-Bind & Point-LLM: Aligning Point Cloud with Multi-modality for 3D Understanding, Generation, and Instruction Following
We introduce Point-Bind, a 3D multi-modality model aligning point clouds with 2D image, language, audio, and video.
Ranked #5 on
3D Question Answering (3D-QA)
on 3D MM-Vet
Less is More: Towards Efficient Few-shot 3D Semantic Segmentation via Training-free Networks
However, the prior pre-training stage not only introduces excessive time overhead, but also incurs a significant domain gap on `unseen' classes.
3D Semantic Segmentation
Few-shot 3D semantic segmentation
+1
Efficient Multi-View Inverse Rendering Using a Hybrid Differentiable Rendering Method
In this paper, we introduce a novel hybrid differentiable rendering method to efficiently reconstruct the 3D geometry and reflectance of a scene from multi-view images captured by conventional hand-held cameras.
Not All Features Matter: Enhancing Few-shot CLIP with Adaptive Prior Refinement
The popularity of Contrastive Language-Image Pre-training (CLIP) has propelled its application to diverse downstream vision tasks.
PointCLIP V2: Prompting CLIP and GPT for Powerful 3D Open-world Learning
In this paper, we first collaborate CLIP and GPT to be a unified 3D open-world learner, named as PointCLIP V2, which fully unleashes their potential for zero-shot 3D classification, segmentation, and detection.
Ranked #2 on
3D Open-Vocabulary Instance Segmentation
on STPLS3D
Conditional Generative Models for Simulation of EMG During Naturalistic Movements
As a solution to this problem, we propose a transfer learning approach, in which a conditional generative model is trained to mimic the output of an advanced numerical model.
Steerable Pyramid Transform Enables Robust Left Ventricle Quantification
Predicting cardiac indices has long been a focal point in the medical imaging community.
Learning to Navigate from Simulation via Spatial and Semantic Information Synthesis with Noise Model Embedding
While training an end-to-end navigation network in the real world is usually of high cost, simulation provides a safe and cheap environment in this training stage.
