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Found 12 papers, 2 papers with code
OpenOcc: Open Vocabulary 3D Scene Reconstruction via Occupancy Representation
We model the geometric structure of the scene with occupancy representation and distill the pre-trained open vocabulary model into a 3D language field via volume rendering for zero-shot inference.
GS-CLIP: Gaussian Splatting for Contrastive Language-Image-3D Pretraining from Real-World Data
3D Shape represented as point cloud has achieve advancements in multimodal pre-training to align image and language descriptions, which is curial to object identification, classification, and retrieval.
PanGu-Draw: Advancing Resource-Efficient Text-to-Image Synthesis with Time-Decoupled Training and Reusable Coop-Diffusion
Current large-scale diffusion models represent a giant leap forward in conditional image synthesis, capable of interpreting diverse cues like text, human poses, and edges.
DreamControl: Control-Based Text-to-3D Generation with 3D Self-Prior
The priors are then regarded as input conditions to maintain reasonable geometries, in which conditional LoRA and weighted score are further proposed to optimize detailed textures.
CoDA: Collaborative Novel Box Discovery and Cross-modal Alignment for Open-vocabulary 3D Object Detection
Open-vocabulary 3D Object Detection (OV-3DDet) aims to detect objects from an arbitrary list of categories within a 3D scene, which remains seldom explored in the literature.
TextField3D: Towards Enhancing Open-Vocabulary 3D Generation with Noisy Text Fields
To this end, an NTFGen module is proposed to model general text latent code in noisy fields.
Towards High-Fidelity Text-Guided 3D Face Generation and Manipulation Using only Images
In this paper, we propose a text-guided 3D faces generation method, refer as TG-3DFace, for generating realistic 3D faces using text guidance.
DiffDis: Empowering Generative Diffusion Model with Cross-Modal Discrimination Capability
DiffDis first formulates the image-text discriminative problem as a generative diffusion process of the text embedding from the text encoder conditioned on the image.
SUIT: Learning Significance-guided Information for 3D Temporal Detection
To this end, we propose to learn Significance-gUided Information for 3D Temporal detection (SUIT), which simplifies temporal information as sparse features for information fusion across frames.
CLIP$^2$: Contrastive Language-Image-Point Pretraining from Real-World Point Cloud Data
Contrastive Language-Image Pre-training, benefiting from large-scale unlabeled text-image pairs, has demonstrated great performance in open-world vision understanding tasks.
Ranked #3 on
Zero-shot 3D Point Cloud Classification
on ScanNetV2
CLIP2: Contrastive Language-Image-Point Pretraining From Real-World Point Cloud Data
Contrastive Language-Image Pre-training, benefiting from large-scale unlabeled text-image pairs, has demonstrated great performance in open-world vision understanding tasks.
LIFT: Learning 4D LiDAR Image Fusion Transformer for 3D Object Detection
LiDAR and camera are two common sensors to collect data in time for 3D object detection under the autonomous driving context.
