3D Object Detection

Dessie can estimate the 3D shape and pose of horses from single images. It also works with other large animals like zebras and cows.

Find3D can segment parts of 3D objects based on text queries.

EgoAllo can estimate 3D human body pose, height, and hand parameters using images from a head-mounted device.

CityGaussian can render large-scale 3D scenes in real-time using a divide-and-conquer training approach and Level-of-Detail strategy. It achieves high-quality rendering at an average speed of 36 FPS on an A100 GPU.

WildGaussians is a new 3D Gaussian Splatting method that can handle occlusions and appearance changes. The method is able to achieve real-time rendering speeds and is able to handle in-the-wild data better than other methods.

[MCC-Hand-Object (MCC-HO)] can reconstruct 3D shapes of hand-held objects from a single RGB image and a 3D hand model. It uses Retrieval-Augmented Reconstruction (RAR) with GPT-4(V) to match 3D models to the object’s shape, achieving top performance on various datasets.

3D Gaussian Splatting can create high-quality 3D scenes in real-time at 1080p resolution with over 30 frames per second. It uses 3D Gaussians for efficient scene representation and a fast rendering method, achieving competitive training times while maintaining great visual quality.

It’s said that our eyes hold the universe. When it comes to the method discussed in the paper Seeing the World through Your Eyes, they at least hold a 3D scene. The method discussed in the paper is able to reconstruct 3D scenes beyond the camera’s line-of-sight using portrait images containing eye reflections.

Robust Dynamic Radiance Fields can estimate both static and dynamic radiance fields along with camera settings. It improves view synthesis from difficult videos, achieving better quality and accuracy than current top methods.