arXiv Machine Learning

MVMD: A Multi-View Approach for Enhanced Mirror Detection

arXiv:2608. 07559v1 Announce Type: cross Abstract: In 3D reconstruction, mirrors introduce significant challenges by creating distorted and fragmented spaces, resulting in inaccurate and unreliable 3D models.

arXiv AI
Sep 7

Reflection-aware Generative Novel View Synthesis

The paper introduces Ref-GeNVS, a training‑free, reflection‑aware approach for generative novel view synthesis in mirror scenes. It treats a mirror image as two complementary views, estimates the mirror plane and reflected camera poses, and uses a two‑stage generation process with Mirror‑gated attention and Reflection injection to produce reflection‑consistent novel views. The method leverages a multi‑view diffusion backbone without finetuning, outperforming recent generative NVS methods on synthetic and real mirror scenes.

By GeonU Kim, Shin Dong-Yeon, Tae-Hyun Oh
arXiv Computer Vision
Sep 4

Fill My Mirror: Geometry-Constrained Mirror Inpainting

The paper introduces a method for mirror inpainting that leverages scene geometry to generate realistic reflections. By estimating the geometry of the fixed scene, the approach projects visible content into the mirror region, reducing the need for hallucination. A two‑mask diffusion strategy then refines the mirror area, balancing geometric constraints with learned priors, and the method operates without training on complex real‑world scenes.

By Ofek Basson, Shimon Vainer, Yacov Hel-Or, Ohad Fried
arXiv Computer Vision
Sep 7

CrossDepth: Geometry-Constrained Attention for Generalizable Multi-View Surround Depth Estimation

CrossDepth introduces geometry-constrained attention for multi-view surround depth estimation, addressing cross-image inconsistencies caused by varying camera intrinsics and limited receptive fields. The method conditions features on per-pixel camera-aware ray embeddings and extends pixel context via cross-image attention limited to geometrically plausible regions. Trained self-supervised with photometric consistency, it achieves better depth accuracy and consistency on DDAD and nuScenes compared to existing self-supervised approaches.

By Samer Abualhanud, Max Mehltretter