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Found 16 papers, 10 papers with code
ControlRoom3D: Room Generation using Semantic Proxy Rooms
Central to our approach is a user-defined 3D semantic proxy room that outlines a rough room layout based on semantic bounding boxes and a textual description of the overall room style.
Cache Me if You Can: Accelerating Diffusion Models through Block Caching
However, one of the major drawbacks of diffusion models is that the image generation process is costly.
Emu: Enhancing Image Generation Models Using Photogenic Needles in a Haystack
Training text-to-image models with web scale image-text pairs enables the generation of a wide range of visual concepts from text.
NeRF-Det: Learning Geometry-Aware Volumetric Representation for Multi-View 3D Object Detection
We present NeRF-Det, a novel method for indoor 3D detection with posed RGB images as input.
3D-CLFusion: Fast Text-to-3D Rendering with Contrastive Latent Diffusion
We note that the novelty of our model lies in that we introduce contrastive learning during training the diffusion prior which enables the generation of the valid view-invariant latent code.
Rotation-Invariant Transformer for Point Cloud Matching
To this end, we introduce RoITr, a Rotation-Invariant Transformer to cope with the pose variations in the point cloud matching task.
Mask3D: Pre-training 2D Vision Transformers by Learning Masked 3D Priors
Current popular backbones in computer vision, such as Vision Transformers (ViT) and ResNets are trained to perceive the world from 2D images.
PCR-CG: Point Cloud Registration via Deep Explicit Color and Geometry
Different from previous methods that only use geometry representation, our module is specifically designed to effectively correlate color into geometry for the point cloud registration task.
RIGA: Rotation-Invariant and Globally-Aware Descriptors for Point Cloud Registration
More specifically, 3D structures of the whole frame are first represented by our global PPF signatures, from which structural descriptors are learned to help geometric descriptors sense the 3D world beyond local regions.
Panoptic 3D Scene Reconstruction From a Single RGB Image
Inspired by 2D panoptic segmentation, we propose to unify the tasks of geometric reconstruction, 3D semantic segmentation, and 3D instance segmentation into the task of panoptic 3D scene reconstruction - from a single RGB image, predicting the complete geometric reconstruction of the scene in the camera frustum of the image, along with semantic and instance segmentations.
Pri3D: Can 3D Priors Help 2D Representation Learning?
Inspired by these advances in geometric understanding, we aim to imbue image-based perception with representations learned under geometric constraints.
Exploring Data-Efficient 3D Scene Understanding with Contrastive Scene Contexts
The rapid progress in 3D scene understanding has come with growing demand for data; however, collecting and annotating 3D scenes (e. g. point clouds) are notoriously hard.
Ranked #8 on
3D Semantic Segmentation
on ScanNet200
RfD-Net: Point Scene Understanding by Semantic Instance Reconstruction
In this work, we introduce RfD-Net that jointly detects and reconstructs dense object surfaces directly from raw point clouds.
Deep Face Forgery Detection
Rapid progress in deep learning is continuously making it easier and cheaper to generate video forgeries.
RevealNet: Seeing Behind Objects in RGB-D Scans
Thus, we introduce the task of semantic instance completion: from an incomplete RGB-D scan of a scene, we aim to detect the individual object instances and infer their complete object geometry.
3D-SIS: 3D Semantic Instance Segmentation of RGB-D Scans
We introduce 3D-SIS, a novel neural network architecture for 3D semantic instance segmentation in commodity RGB-D scans.
Ranked #3 on
3D Semantic Instance Segmentation
on ScanNetV2
