Search Results for author:
Found 7 papers, 4 papers with code
NVS-Adapter: Plug-and-Play Novel View Synthesis from a Single Image
Transfer learning of large-scale Text-to-Image (T2I) models has recently shown impressive potential for Novel View Synthesis (NVS) of diverse objects from a single image.
Stable and Consistent Prediction of 3D Characteristic Orientation via Invariant Residual Learning
Learning to predict reliable characteristic orientations of 3D point clouds is an important yet challenging problem, as different point clouds of the same class may have largely varying appearances.
PeRFception: Perception using Radiance Fields
The recent progress in implicit 3D representation, i. e., Neural Radiance Fields (NeRFs), has made accurate and photorealistic 3D reconstruction possible in a differentiable manner.
Self-Supervised Learning of Image Scale and Orientation
We study the problem of learning to assign a characteristic pose, i. e., scale and orientation, for an image region of interest.
Fast Point Transformer
The recent success of neural networks enables a better interpretation of 3D point clouds, but processing a large-scale 3D scene remains a challenging problem.
Ranked #24 on
Semantic Segmentation
on S3DIS
Efficient Point Transformer for Large-scale 3D Scene Understanding
Although sparse convolution is efficient and scalable for large 3D scenes, the quantization artifacts impair geometric details and degrade prediction accuracy.
Self-Calibrating Neural Radiance Fields
We also propose a new geometric loss function, viz., projected ray distance loss, to incorporate geometric consistency for complex non-linear camera models.
