Welcome to the Best of 3DV series, your virtual pass to some of the groundbreaking research, insights, and innovations that defined this year’s conference. Live streaming from the authors to you. View more CV events here.

Attending the 3D Vision Conference means confronting 177 accepted papers across 3.5 days, far more than any one person can absorb. Skimming titles the night before isn't enough.

This talk shows how to build a systematic, interactive map of an entire conference using modern open-source tools. We load all 177 papers from 3DV 2026 (full PDF page images plus metadata) into a FiftyOne grouped dataset. We then run three annotation passes using Qwen3.5-9B on each cover page: topic classification, author affiliation extraction, and project page detection. Document embeddings from Jina v4 are computed across all 3,019 page images, pooled to paper-level vectors, and fed into FiftyOne Brain for UMAP visualization, similarity search, representativeness scoring, and uniqueness scoring.

The result is an interactive dataset you can query, filter, and explore in the FiftyOne App. Sort by uniqueness to find distinctive work, filter by topic and sort by representativeness to understand each research area, and cross-reference with scheduling metadata to build a personal agenda.

I demonstrate the end-to-end pipeline and discuss design decisions regarding grouped datasets, reasoning model output parsing, and embedding pooling strategies.

We present SeeingThroughClutter, a method for reconstructing structured 3D representations from single images by segmenting and modeling objects individually. Prior approaches rely on intermediate tasks such as semantic segmentation and depth estimation, which often underperform in complex scenes, particularly in the presence of occlusion and clutter. We address this by introducing an iterative object removal and reconstruction pipeline that decomposes complex scenes into a sequence of simpler subtasks. Using VLMs as orchestrators, foreground objects are removed one at a time via detection, segmentation, object removal, and 3D fitting. We show that removing objects allows for cleaner segmentations of subsequent objects, even in highly occluded scenes. Our method requires no task-specific training and benefits directly from ongoing advances in foundation models. We demonstrate state-of-the-art robustness on 3D-Front and ADE20K datasets.

Generating dynamic 3D content that moves and deforms over time is a key frontier in visual computing, with applications in VR/AR, robotics, and digital humans. In this talk, I present our series of works on physically realistic 4D generation: from neural surface deformation with explicit velocity fields (ICLR 2025) to our 4Deform framework for robust shape interpolation (CVPR 2025). Both methods use implicit neural representations with physically constrained velocity fields that enforce volume preservation, spatial smoothness, and geometric consistency. I will also introduce TwoSquared (3DV 2026, oral), which achieves full 4D generation from just two 2D image pairs — demonstrating a practical path toward controllable, physically plausible 4D content creation.