Search Results for author:
Found 10 papers, 5 papers with code
3D Congealing: 3D-Aware Image Alignment in the Wild
The framework optimizes for the canonical representation together with the pose for each input image, and a per-image coordinate map that warps 2D pixel coordinates to the 3D canonical frame to account for the shape matching.
Learning the 3D Fauna of the Web
We show that prior category-specific attempts fail to generalize to rare species with limited training images.
ZeroNVS: Zero-Shot 360-Degree View Synthesis from a Single Image
Further, we observe that Score Distillation Sampling (SDS) tends to truncate the distribution of complex backgrounds during distillation of 360-degree scenes, and propose "SDS anchoring" to improve the diversity of synthesized novel views.
Learning a Room with the Occ-SDF Hybrid: Signed Distance Function Mingled with Occupancy Aids Scene Representation
We found that the color rendering loss results in optimization bias against low-intensity areas, causing gradient vanishing and leaving these areas unoptimized.
RICO: Regularizing the Unobservable for Indoor Compositional Reconstruction
Recently, neural implicit surfaces have become popular for multi-view reconstruction.
E-NeRV: Expedite Neural Video Representation with Disentangled Spatial-Temporal Context
However, the redundant parameters within the network structure can cause a large model size when scaling up for desirable performance.
Ranked #4 on
Video Reconstruction
on UVG
Searching Parameterized AP Loss for Object Detection
In this paper, we propose Parameterized AP Loss, where parameterized functions are introduced to substitute the non-differentiable components in the AP calculation.
MaIL: A Unified Mask-Image-Language Trimodal Network for Referring Image Segmentation
Referring image segmentation is a typical multi-modal task, which aims at generating a binary mask for referent described in given language expressions.
Ranked #1 on
Referring Expression Segmentation
on G-Ref test B
Searching for TrioNet: Combining Convolution with Local and Global Self-Attention
In order to effectively search in this huge architecture space, we propose Hierarchical Sampling for better training of the supernet.
Learning Part Segmentation through Unsupervised Domain Adaptation from Synthetic Vehicles
We believe our dataset provides a rich testbed to study UDA for part segmentation and will help to significantly push forward research in this area.
